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FeaturingDne1.0
Effective Noise Reduction & Detail Optimization
An Analysis & Post Capture Processing Model
White Paper
nik multimedia, Inc.
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Table of Contents
Introduction
The Dependent Nature of Noise ............................................4
Dilemmas of Techniques.......................................................5Science Versus Art ...............................................................5
The Evolutionary Nature of Noise Reduction...........................6
The Proposed Solution Dne ...........................................6
An Introduction To Noise
Noise and its Origins ............................................................ 7
Identifying Noise and Common Terms.................................... 7
Shot Noise .........................................................................8
Read Noise ........................................................................8
Fixed Pattern Noise..............................................................8
Other Common Terms..........................................................9
Luminance Versus Chrominance Noise ..................................9
Identifying Noise..................................................................9
Identifying Noise Using Photoshop Techniques..................... 10
Noise and the Camera .......................................................10
Image Detail and its Relationship in Noise Reduction............. 11
Removing Versus Reducing Noise ....................................... 11
Common Methods For Addressing Noise
Dealing with Fixed Pattern Noise......................................... 12
Working with Aberrant Hot Pixels ........................................ 12Common Methods for Reducing Hot Pixels ........................... 13
The Threshold Dilemma ....................................................14
Reducing Noise While Sharpening......................................14
Technical Methodologies For Noise Reduction
Blur Variations................................................................... 16
Median Filter..................................................................... 17
Fourier Transformation ....................................................... 18
In-Camera Noise Management: Practical ConsiderationsAdvantages....................................................................... 19
Issues & Challenges ............................................................20
The Permanent Nature of Noise Reduction............................20
Double Processing and Workow ........................................20
The Need for Processing Power..........................................20
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Undesirable Effects of Reducing Noise
Blind Area Artifacting ........................................................ 21
Detail, Noise Relationships, and Blind Area Artifacting ...........22
Contrasting Noise and Detail...............................................22
Remaindered Pixels ...........................................................23
Painterly Effect..................................................................24Blurring Effect ...................................................................25
Resolution Issues: Screen Versus Print Images ...................... 25
Print Optimized Versus Screen View Presentation.................... 25
The Non-Scientic and Subjective Nature of Perception ........ 26
Details and the Power of the Human Eye ............................. 27
Practical & Subjective Issues in Noise Reduction ..................... 27
Optimized Noise Reduction
1. Previewing and Analyzing the Image ...............................30
Auto-Detection of Noise in Sensitive Areas ...........................30Multi-Preview Mode ........................................................30
Analysis Mode: Screen-Based Image Analysis Tools ................. 31
Grab and Drop Preview.................................................... 31
2. Optimizing Images Based on a Specic Camera................ 31
The Unique Nature of Digital Cameras & Image Details ............ 31
The Detail-to-Color Correlation...........................................32
Targeted Reduction and the Camera Prole Controller............. 32
3. Reducing Color Noise and Maintaining Color Details .......... 33
Blurring Lab Channels Versus Dne Detail Protection.............. 33
4. Balancing Detail and Color in Noise Reduction.................. 35
The Relationship of Noise, Detail, and Artifacts ...................... 35
JPG Reprocessing for Artifact Reduction ............................... 36
5. Selectively Reducing Noise Quickly and Intuitively ............ 37
Selectively Removing Hot Pixels in Dark Scenes ..................... 37
Pressure Sensitivity & Selective Reduction ............................ 37
6. Optimizing Color Changes and Relationship ......................38
Color Corrections: Considering Detail and Noise ..................... 38
Color and Colorcast Adjustments .......................................38
7. Controlling Light and Contrast in Image Details................. 39
Tonal Corrections and Noise ..............................................39
Highlights & Shadows and Light Adjustments .........................39
Adjusting for Counter-Light .........................................40
Highlights & Shadows ................................................40
Dne Tonal Adjustments (Levels) ................................41
More Information ..............................................................42
Dne System Requirements .............................................42
Copyright 2002 2003 nik multimedia, Inc. All rightsreserved. Some implementations discussed in thispublication are proprietary in nature and included inpending patents. nik multimedia, the nik multimedialogo, Dne, and nik Sharpener Pro are trademarks ofnik multimedia, Inc. All other trademarks and brandnames are the property of their respective owners.
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Introduction
4
Introduction
Noise reduction, from a photographic perspective, is a
problem without a universally accepted solution. There is no
single established routine, no one set of algorithms, no magic
bullet that reduces or eliminates noise while maintainingdetail. Reducing noise optimally involves a combination of
considerations that include the capture device (the camera),
the nature of noise and image details, and the non-scientic
nature of perception. With this said, the single answer to the
question of how noise should be reduced in an image isIt
depends.
The Dependent Nature of Noise
Noise reduction depends on so many variables that a single
mathematical process alone cannot reduce noise effectively.
Photographic details and the natural characteristics of the
image do not factor into one single algorithm or mathematical
solution. Optimal noise reduction, whatever the process, must
protect detail and maintain the natural appearance of the
entire image. Achieving that result depends on effectively
dealing with variables that originate at various points in the
imaging process, from capture to the nal presentation of the
image.
While some aspects of noise are predictable, others are
random. While some can be dealt with at the time of capture,
others must be dealt with post-capture methods. Dealing
with noise in the printed or projected image is a matter of
judgment. Considering this, noise reduction in the post-capture
stage becomes the most effective approach.
The current state of the art for the post-capture stage of
noise reduction lacks an effective solution that takes into
account both the dynamic nature of photographic detail as
well as the importance that workow plays in the creation of
a quality digital image. Many tricks and techniques are often
applied piecemeal to an image to deal with particular noise
problems in an image. Scripted, pre-dened, and recorded
image editing routines are popular remedies for noise, all of
which differ based on an individuals experience. The "tips and
tricks" approach to noise reduction is an acknowledgement of
the reality that there is no coherent process or combination of
Optimal noise reduction,
whatever the process, must
protect detail and maintain
the natural appearance of
the entire image.
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Introduction
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algorithms that can globally eliminate noise in an image.
Dilemmas of Techniques
While many of the tricks and techniques used in image editing
can be effective in reducing noise in particular images, they
commonly create undesirable side effects that destroy detail
and the effectiveness of an otherwise good image. Some are
better than others, but a single solution that effectively reduces
noise across the broadest range of images without creating
the unwanted side effects does not exist. In this paper, we
discuss a variety of techniques and methodologies as well as
their side effects. One broadly used technique discussed in this
paper is the use of a threshold. Certain implementations of
a threshold within a noise reduction solution present distinct
trade-offs. Other techniques, such as those that utilize blurring
or averaging approaches, soften or alter detail in order to mask
noise or substitute detail in the image.
A pragmatic approach to noise reduction considers the
image in the nal print and sets out to maintain the natural
photographic characteristics of the image while addressing the
problem of noise. This approach emphasizes the importance of
the quality of the image as dened by the details that appear
across the entire image in its nal stage, the print.
Science Versus Art
Effective photographic optimization of detail in a digital
image involves providing maximum detail presentation while
avoiding a digitally processed appearance. Reducing the noise-
to-detail ratio at the capture and signal processing stage is
device dependent, and many cameras address the problem
of noise reduction, some more effectively than others. At the
post-capture phase, however, managing an acceptable level
of noise reduction and maintaining optimal detail are opposing
concepts. At the pixel level, detail and noise are structurallythe same. Noise and detail are dened by how they are
perceived in the image. The human eye makes that distinction.
If the pixel distracts from the detail in the image, the detail
is unwanted in its current state; if the pixels contribute to
effective detail in the image, the noise may be perceived as
detail, and thus is an important part of the image. An effective
approach to noise reduction hinges on the perception of the
At the post-capture phase,
managing an acceptable
level of noise reduction
while maintaining optimal
detail are opposingconcepts, making optimal
noise reduction an even
further challenge.
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Introduction
6
viewer, assessing the balance between the optimal level of
detail and the acceptable level of noise in the print. In a sense,
this concept is contrary to commonly used scientic methods.
Where most scientic approaches treat the signal (a necessary
step) and then process the image to reduce noise, the proposed
concept includes considerations from the signal processing stage
but reserves effective noise reduction and detail optimization
for the post-capture process. In other words, the proposed
solution considers the nal image and moves backwards to
the capture process to consider the sources and nature of
the noise in order to treat the image with a combination
of imaging science tools. In doing so, a combination of a
scientic approachusing effective tools to analyze and treat
the imageand visual tools will result in an optimal noise
reduction-detail optimization system.
The Evolutionary Nature of Noise Reduction
While some types of noise in a digital image are unavoidable,
given the nature of light and the technology of analog to digita
transfer, other kinds of noise arise from the capture device. The
larger and more advanced the cameras sensor (CCD or CMOS)
the lower the level of noise. As image sensors in cameras
and their technologies advance, noise and its appearance will
change.
The Proposed Solution Dne
Dne was developed based on a study of noise that focused
specically on how noise is manifested in the image. That is, we
approach noise from a photographic perspective and focus on
how noise appears in the image. We focus on noise and image
details from a photographic perspective and propose a solution
that is effective, considering the inevitable nature of noise and
the evolution of noise reduction in capture devices.
This paper evaluates digital noise, its role in the digital image,and methods for compensating for noise with a specic focus on
maintaining the photographic tendencies and natural balance
of a digital image, and introduces Dne as a solution for
effective noise reduction and detail optimization.
The manner in which
images are captured,
assembled, and processed
by digital cameras will
constantly evolve, making
it even more important forpost-capture noise solutions
to be able to evolve with
capture technology.
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Introduction to Noise
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An Introduction To Noise
Noise and its Origins
Noise in a digital image consists of visible errors, which are
created by an electrostatic charge within the camera and a
process that converts an analog signal to its digital elements.These errors are transferred to the image as part of the detail
of the image and appear as bright, colored, or dark specks. As
such, noise is actually detail in the image that does not appear
as it is expected due to an error in the image capture process.
Various factors affect noise, ranging from the presence of
light at the time of capture, exposure time, sensor (CCD or
CMOS) temperature, and the manner in which the cameras
sensor processes the image. These errors appear in print and
on screen as distracting aberrations which, when visible to
the human eye, distract the viewer and create an unnatural
appearance.
There are a variety of terms used to describe noise and their
sources. We will make distinctions about the nature of noise
based on its origin as well as its appearance in the nal image.
We make these distinctions to clarify the current state of
technology while acknowledging the necessary compromise
in balancing noise with optimal detail. Understanding the
origins of noise and how it appears in the image is the rst
step in determining how to reduce noise and create an
optimal, balanced image. The objective of this discussion is
to understand the nature of noise in digital photography, to
accept the current state of technology, and to encourage the
use of appropriate tools in optimizing workow to achieve the
best possible image.
Identifying Noise and Common Terms
Various terms are used to identify noise in a digital image.
Often the terms used to describe noise relate to their origins
while others relate to the appearance of noise. It is important
to make clear distinctions in dening terms and their
applications when discussing noise in the digital image. Grain,
for example, is often used to describe the appearance of noise
in a digital image, even when grain, a term derived from lm
images, is not a factor in the digital capture process.
Common terms like
grain are often misused
regarding noise reduction,
making the distinction
between noise reduction
and digital grain
reduction even more
difcult.
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Introduction to Noise
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Shot Noise
Shot noise is the most apparent type of digital image noise.
Shot noise is a pattern of dark, bright, or colorful specks that
is often best visible against plain areas, such as sky. Shot noise
will be present to some degree in every digital image because
of the random nature of light photons and sensor electrons.
Because of the random nature of light, when a digital image
is captured, not every sensor in the CCD will be struck by the
same amount of photons. Because shot noise is caused by light,
the more light, the more shot noise. However, an interesting
phenomenon occurs as light increases: the shot noise seems to
plateau because it tends to merge with Read Noise (explained
below) as they cancel each other out. As light increases, the
distraction from detail which shot noise causes in a portion
of an image will seem to be negligible to the viewer. Viewedacross the image as a whole, however, Shot Noise can be
distracting and appear unnatural to the viewer.
Read Noise
Read Noise is another common term that is used to identify
noise based on its source. Read Noise is generated by the
digital cameras processor and is analogous to the electronic
noise one might hear when listening to recorded music.
Sometimes called amp noise, Read Noise originates in the
processor and is generated by random electrons that excite the
pixels in the sensor array causing them to misre, resulting in
chrominance noise or luminance noise. Ironically, the better the
camera, the more powerful the amplier, the more powerful
the amplier, the more read noise. While the photographer has
no control over Read Noise caused by the amplier, two other
sources of read noise are within the photographers control.
Read Noise increases as the temperature inside the camera
increases. It also increases with longer exposures and with high
ISO ratings. Camera design that keeps CCD and CMOS sensorscool continue to address the problem of Read Noise with some
success.
Fixed Pattern Noise
Fixed Pattern Noise is another term frequently used to identify
a specic type of noise. Fixed Pattern Noise is the only noise
that is not completely random. Because the pixels in the sensor
Read Noise and the CCD
After each shot, the cameras sensors
are reset to a zero position. However,
this resetting process may not always
be uniform. The noise that is generated
is referred to as Reset Noise. When the
process of reading out the values of
the CCD extends over multiple clocks
(processing time units of the processor
that reads the CCD) the sensors are not
read simultaneously. When that occurs
noise can be created simply because
the time between capture and readout
is not uniform for the entire chip.
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Introduction to Noise
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array are not uniform in size, spacing, and efciency, very
slight errors are produced by each pixel each time an image
is captured. These pixel errors repeat themselves in each
photo taken, making this particular kind of noise created by an
individual camera predictable. This xed pattern noise will
vary only slightly in intensity from photo to photo, increasing as
light in the image decreases.
Other Common Terms
In coping with the problem of noise in digital images, users
have come up with a number of terms to describe what they
see in the image. The term Hot Pixel,for example, is used to
describe an obvious hot spot in the image. Hot Pixels can
appear as either bright white pixels or as colored spots in the
image and come from any error or misring of a pixel. The
term Hot Pixel is used more often to describe the appearance
of noise rather than its cause.
Luminance Versus Chrominance Noise
In addition to dening noise at its origins, we nd it useful to
dene noise as it appears in the image as Luminance Noise
and Chrominance Noise. Scientic papers and articles often
use these terms to refer to brightness and color. We use these
terms to distinguish noise based on its characteristics, which
is the way that we address the issue of noise. We dene noisewith no appearance of color as Luminance Noise, as it manifests
itself as purely dark or bright white noise. We use the term
Chrominance Noise, on the other hand, to describe noise that
consists of some degree of color.
These distinctions are important in the process of reducing
noise in the post capture process. As we evaluate and analyze
noise reduction, it is essential to identify noise as it appears
in the image, rather than how it is generated. This principle
becomes an important aspect of optimizing image details whilereducing noise effectively for the nal presentation of the
image.
Identifying Noise
Visually identifying noise in an image is an important aspect
of detail optimization and noise reduction. When images
are captured and viewed, noise often appears as subtle and
To effectively address
noise in the post-capture
stage of image editing, it is
essential to identify noiseas it appears in the image,
rather than how it was
generated.
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Introduction to Noise
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Noise often exists at the
same intensity level across
an image, while appearing
more dominant in onearea, such as sky, and less
apparent in foliage and
other high detail areas.
indistinguishable aspects of the image. This is due in part to
the fact that noise can appear in a combination of random and
xed characteristics with a range of intensity. In other words,
noise does exist in all digital images, and its impact on the
image needs to be considered visually as it relates to detail
in the image. And because detail is closely related to noise,
it becomes increasingly important to be able to locate and
identify noise. The crucial aspect of noise reduction in the post
capture process is identifying noise and distinguishing noise
from detail.
Identifying Noise Using Photoshop Techniques
There are a variety of basic techniques for identifying noise in
image editing applications. The following routine provides a
basic method for identifying noise in a digital image.
1.Open an image that has a low detail area, such as a sky or plain
background that has been captured in moderate to low light.
2.Crop the image to show the plain or low detail area.
3.Within Photoshop, access the High Pass lter from the Filter
menu (Filter 4 Other 4 High Pass) and apply the lter with a
radius of 3 pixels.
4.Hold Control + Shift + L keys to apply Auto Levels, which
increases the contrast of the High Pass, making details more
visible. At this point, both Chrominance and Luminance noise arevisible.
5.To isolate the Chrominance Noise and show the Luminance
Noise, hold Control + Shift + U.
Noise and the Camera
Theoretically, every camera captures images in the same way:
light strikes a grid of sensors and those sensors translate the
light into a digital image. The light waves (photons) that create
the image are not digital in naturethey either strike or do
not strike a sensor on the CCD or CMOS. The analog signal
coming into the camera must be converted to a digital signal
by an array of sensors in the back of the camera. The manner
in which a particular camera captures the signal and converts
it into a digital image determines to a large degree the type
and degree of noise which will be present when the image is
printed. Behind the CCD or CMOS lies electronics that dene,
order, and amplify the digital signal, causing particular kinds
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Introduction to Noise
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of noise. It is important to understand how shooting conditions
and the capture process affect noise. It is also important to
learn to recognize noise and its relationship to detail in the
postcapture noise reduction process.
Image Detail and its Relationship in Noise ReductionAs mentioned earlier, deciding what is considered noise and
what is considered detail in an image is crucial to a pragmatic
approach to noise reduction. Within the noise reduction
process, it is essential to judge noise, not on the characteristics
of the individual pixel, but rather in the context of detail in the
image. Focusing on the information at the pixel level denies the
context of the image, which offers essential information to the
treatment of detail. By looking at noise in context, in relation
to the detail surrounding it, we see the image more from a
photographic perspective.
While our approach to noise reduction and detail optimization
is based on a range of imaging science practices, the proposed
solution offered by Dne relies on photographic principles
which are applied from a post capture perspective and involve
key concepts related to digital imaging as well as photography.
Removing Versus Reducing Noise
From a photographic perspective, the concept of noise removal
is useless. Photographic detail and noise are inextricably linked
in every image.
As a result, the concept of noise removal is neither optimal
nor practical. Rather, reducing noise while balancing and
maintaining important detail leaves the image more natural.
Removing or eliminating the presence of noise is contrary
to obtaining a natural-looking photograph. Reducing versus
removing noise is a key distinction in effective noise reduction
and detail optimization that leads to better digital images.
From a photographic
perspective, the concept of
noise removalis useless.
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Common Post Capture Methods for Addressing Noise
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Common Methods For Addressing Noise
Noise reduction is a compromise that addresses the presence of
wanted versus unwanted detail. The following section outlines
common methods for reducing noise under certain conditions.
As with all methods and techniques for reducing noise in thepost-capture process, each addresses a limited range or type of
noise that occurs within the digital image.
Dealing with Fixed Pattern Noise
Fixed Pattern Noise, noise that is generated within the camera
itself, is a byproduct of all digital images. Fixed Pattern Noise
appears in images that were captured in any degree of light.
When present in dark or night shots, noise can often be even
more visible because of the dark background of the image.
Dark Frame Subtraction is a typical method for reducing Fixed
Pattern Noise in images that were captured under extreme low
light conditions. Dark Frame Subtraction can be an effective
method when targeting the Fixed Pattern Noise that occurs
within the camera due to long exposures under low light
conditions.
The Dark Frame Subtraction process is a lengthy and tedious
process that involves capturing the image, then creating a
dark and empty frame with the camera (shortly after the
initial capture), and then using the dark frame in the image
editing process to remove the pixel errors that occurred
when capturing the image. Dark Frame Subtraction has some
advantages for images captured under low light, but it is not a
solution for noise from other sources.
Working with Aberrant Hot Pixels
Hot Pixel is a general term used to describe bright or colored
specks in an image. As the term indicates, a hot pixel is a
pixel that is overcharged and misres, destroying detail in
the image. Generally, hot pixels appear in a xed pattern in
the image and appear more frequently in images that were
captured in low light conditions.
The pixels in the sensor array of a digital camera are all
sensitive to light to varying degrees, and some are more
sensitive than others. Open a shutter on any camera long
enough and some of the more sensitive pixels will begin to re.
Dark Frame Subtraction
uses an image capture
with a complete lack of
light, an empty image,to reproduce the pattern of
noise, which can be used to
replace or alter misred
pixels in the image.
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Common Post Capture Methods for Addressing Noise
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The longer the exposure, the more hot pixels, and the higher
the temperature of the camera, the more hot pixels. Under
bright lighting, hot pixel noise does not appear as frequently
and is not apparent because fewer pixels misre.
Common Methods for Reducing Hot PixelsThere are various tricks and techniques for reducing hot pixels
that range from Dark Frame Subtraction, discussed earlier, to
a number of methods that use pixel averaging and the Median
lter based on the consideration of pixel characteristics.
The latter two methods provide solutions in two very different
ways: calculating the average of a group of pixels versus
selecting the most representative pixel of a group of pixels.
If the averaging algorithm is applied to an entire image, theimage will appear blurred. Applying the Median lter, on the
other hand, will blur the image to a lesser degree, as straight
edges are not blurred.
When reducing a single hot pixel on a dark background, for
example, a pixel averaging approach raises the calculated
average of the pixel so that the luminosity between the color
of the hot pixel and the background is calculated and applied.
When pixel averaging is used on hot pixels, the corrected hot
pixels do not disappear; rather, they are simply blurred and areless distinguishable in context.
Other methods that rely on the Median lter utilize an
algorithm that focuses on a group of pixel blocks3x3
(9 pixels), 5x5 (25 pixels), or 7x7 (49 pixels) or more.
These methods select the one pixel from the cluster that is
most representative and replaces the hot pixel with the
representative one.
While the Median lter approach is often preferred over a pixel
averaging approach, both the Median lter and pixel averaging
often create plain and unnatural structures in the image. Even
more signicant, if the selected image contains any signicant
degree of random noise, a Median lter frequently introduces
unnatural structures in plain or low detail areas.
Although only one element
of a sensor misres, in
most cases, a hot pixel is
not a single pixel but maybe a cluster of as many as
12 pixels.
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Common Post Capture Methods for Addressing Noise
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The Threshold Dilemma
A variety of techniques for reducing or removing noise use
what is commonly known as a threshold. When a contrast
threshold is used to differentiate noise pixels from detail pixels,
the result is frequently an unnatural image change. When a
threshold is set in an image processing calculation, a threshold
level of detail is set and the specied calculations are applied
to detail that is either above or below that threshold. For this
reason, neither a hard or soft threshold should be used to
differentiate noise from detail.
While the use of a threshold setting can be effective to some
extent when used in the process of reducing the appearance of
noise, a hard threshold presents even greater problems from
a photographic perspective. When used in the noise reduction
process, some noise is distinguished from detail and some is not,
depending on the threshold setting and the corresponding size
of the detail. Detail that falls below the threshold is considered
and processed, and detail that is above the threshold is
left untouched. It is here that the problem often arises. The
effect of using a threshold is that the size of detail has no
relationship to the details photographic signicance. Small
detail may appear as noise, or it may appear as important
detail that signicantly contributes to the photographic natureof the digital image. At best, using a method that uses a hard
threshold to reduce noise is a compromise.
Reducing Noise While Sharpening
The use of a threshold is a common topic with regard to noise
reduction, especially when combined with image sharpening.
There are books full of techniques for noise reduction and
image sharpening, and many of these include methods that
employ the use of a threshold setting. Many of these noise
reduction methods often include an option to apply an Unsharp
Mask lter to the image as part of the overall noise reduction
process. The rationale for including this in the noise reduction
process is two fold. First, the objective is to bring back some of
the detail that is lost in the noise reduction process. Second,
this process seems to reduce noise by sharpening larger details
while leaving smaller details (including any residual noise)
unsharpened.
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Common Post Capture Methods for Addressing Noise
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This technique sounds completely rational and effective. In
limited cases with selective application, this method can be
useful, as larger details appear sharper and the smaller details,
including residual noise, are less apparent because they are not
sharpened.
However, the major problem created by this process, from a
photographic perspective, is obvious. Within this process, details
are ltered and considered, not based on their photographic
importance, but rather on their size relative to the hard
(or set) threshold. The result is that the image is sharpened
in a haphazard sense without regard to the importance of
specic details. The impact on the image can be seen more
clearly in print than on the screen, as some detail edges will
be sharpened and others not, regardless of their photographic
signicance.
The process of employing sharpening during the noise reduction
process also raises a signicant workow issue. Without specic
information related to the exact sharpening that was performed
on an image, sharpening is an enhancement that cannot be
undone or compensated for once it is applied. Without this
information and without a method for unsharpening the image,
it is not possible to remove the sharpening performed on an
image within the image editing process.
Most importantly, image sharpening should be performed after
the image editing process and just prior to the print process.
Applying any signicant degree of sharpening to the image in
the noise reduction process leaves the image far less susceptible
to an optimal image sharpening process, whether an image is
sharpened via an image editing application or within any RIP
(Raster Image Processor) process prior to output.
Using a threshold setting
uniformly relies on an
algorithm to alter details in
an image regardless of the
photographic signicance
of a specic detail.
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Technical Methodologies For Noise Reduction
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Technical Methodologies
Post-capture methods for noise reduction may use any one or
a combination of a number of detail modication processes
that range from blurring and softening pixel edges to altering
the color and/or luminosity of one or more pixels. A varietyof noise reduction methods are frequently used in software
applications, with many methods being combined in the process
of reducing noise.
Blur Variations
Pixel blurring variations are among the most common
methodologies for reducing noise. Various types of blurring
techniques can be used, many of which are implemented as
lters. These approaches often inspect a group of pixels around
a target pixel that is being processed, calculate an average for
that group of pixels (possibly weighing some pixels more than
others), and then apply a level of blurring. Blurring variations
can be effective in reducing the appearance of smaller noise,
but frequently it leaves important details blurred and out of
focus.
The most common blur variation is the Gaussian Blur lter,
which weights the group of pixels using the Gaussian bell curve.
Applying this bell-shaped image lter is similar to cropping
away the highest frequencies in a Fourier transformed image.
When applied, the effect is similar to an out-of-focus lter,
although it is different from a conventional out-of-focus effect
created in-camera.
There are two limitations to using the Gaussian Blur lter for
noise reduction. The rst and most well known limitation is
that when the Gaussian Blur is applied to an image globally,
it eradicates wanted details along with unwanted details.
Secondly, when the Gaussian Blur is applied without qualitative
input from the user, the detail softening that occurs is
difcult to integrate or combine into the original image while
maintaining a natural appearance. That is, there is no natural
way to objectively evaluate and mix a blurred effect (in
those areas were noise reduction is necessary) with the original
(those areas where no noise reduction is wanted) so that the
image looks natural.
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Technical Methodologies For Noise Reduction
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While a modied version of Gaussian Blur can be effective
when implemented in a fashion that considers photographic
characteristics of an image, a global application of the Gaussian
Blur suffers from the side effects of many noise reduction
processes.
Median Filter
The Median lter is also a common method for reducing noise
in a digital image. The Median lter inspects a number of pixels
(from 10 to 100) around the current pixel, then sorts that list
of pixels, selecting the middle pixel as the replacement pixel.
To some, the Median lter may appear to be identical to the
blurring method above. However, while the blurring effect
described above calculates the average of the inspected pixels,
the Median lter simply selects the pixel in the middle of the
sorted list. So if 100 pixels are considered with 51 black and 49
white pixels, the Median lter will select a black pixel, while
a blurring lter would return the average of those 100 pixels,
resulting in gray.
Typically, a Median lter inspects the pixels within a square of
a given radius. If applied with a radius of 5, for example, a list
of 121 values needs to be sorted for each pixel, often requiring
signicant processing power.
The Median lter also presents other potential issues when it is
used for global noise reduction purposes. Unlike the Gaussian
Blur, the Median lter is a non-linear lter that can alter the
image adversely when applied to details that are susceptible
to artifacting. Even when applied to a small degree, the
Median lter can introduce unpredictable, unnatural, and often
unwanted structures into certain detail types in an image.
Use of a Median lter can introduce painterly structures in an
image, which resemble small details in a watercolor painting,
or it may create faceting structures, which in some cases can
be even more distracting than the original noise. As a result,
the Median lter serves only a limited purpose as a technical
solution for selected noise reduction for specic detail types.
When applied globally,
the Median lter affects
a range of detail types
differently and can
introduce painterly
structures or faceting
artifacts to in an image.
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Technical Methodologies For Noise Reduction
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Fourier Transformation
Of the most commonly implemented methods for reducing
noise, the more complex approach is to Fourier Transform the
image into a space where the image is no longer represented
by pixels or vectors. Instead, the image is stored as frequencies
and then image corrections are made. When using the Fourier
Transformation, it is easier to locate certain structures in an
imagesuch as lines, edges, and patternssince noise does not
typically form such structures. A Fourier Transformation can be
used to distinguish details from noise in some cases; however,
as promising it sounds, this method is not the silver bullet for
noise reduction.
The problem is that this approach only works well in theory.
It can successfully differentiate between random noise andactual image details, especially details that are repeated or
are regular patterns. However, in actual digital images, the
frequency analysis method (the Fourier Transformation) too
often appears to interpret patterns as noise in areas that
actually dont contain any patterns. As a result, this approach
often leads to unwanted structures being introduced into the
image.
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In-Camera Noise Management
Image processing at the time of capture provides unique
advantages and challenges when addressing the issue of noise
in the digital image. An image is created as it is captured
and interpreted at the signal processing level (the imagecreation process within the camera). During the creation, the
manner in which the sensor (CCD or CMOS) creates the image
includes techniques to maximize detail while minimizing noise.
However, noise is, nevertheless, present in the image.
The challenges for reducing noise are faced equally in the
process of in-camera noise reduction. As you would expect,
detail and noise have the same close relationship in the capture
stage as when the image is captured and processed by the
camera. Among other considerations, the more signicantdangers from in-camera noise reduction are detail loss from the
noise reduction process and the danger of future loss of detail
(via repeated noise processing) in the post-capture image
editing stage.
Advantages
There are limited advantages to in-camera noise reduction.
In-camera noise reduction is done during an image-processing
phase within the camera that can adjust for specic variables
that affect noise. Variables such as CCD or CMOS temperature
and the ISO setting of the camera affect the presence of noise
and can be compensated for within the image-processing
phase. For example, if the CCD or CMOS temperature is T1 and
the ISO setting is I1, the camera can adjust the noise reduction
process based on known behaviors of the CCD or CMOS at T1
and at I1. If an image is captured again with the same CCD or
CMOS temperature of T1 but with a new ISO setting of I4, the
camera can adjust the noise reduction for that specic image
based on those variables and the known behaviors of the image
sensor (CCD or CMOS).
There are, however, disadvantages to utilizing noise reduction
within the camera, ranging from the lack of necessary
processing power within the camera for effective noise
reduction to the permanent loss of detail and the lack of
control over detail reduction.
There are disadvantages
to utilizing noise reduction
within the camera, ranging
from the permanent loss of
detail to the lack of control
over detail reduction.
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Issues & Challenges
The Permanent Nature of Noise Reduction
Loss of detail is among the most signicant side effects of in-
camera noise reduction, as noise reduction within the capture
device modies image detail. Just as with certain image
corrections, such as contrast and sharpening adjustments, noise
reduction is an image change that cannot be undone by the
user once applied to the image. These losses of detail occur
when algorithms in the camera remove image details that
it considers noise. This detail is lost even before the picture
is recorded to the cameras memory card. This discarding of
image detail is performed automatically and it is often based on
objective calculations that are not able to distinguish between
true photographic detail and noise.
Double Processing and Workflow
Among the challenges of in-camera noise reduction is
controlling its effect on image detail, especially its detrimental
effect on the image from a workow standpoint. Most
importantly, in-camera noise reduction limits the ability of the
user to optimize detail once noise reduction has been applied
inside the camera. Once the camera has made a determination
of what is noise and what is detail and then has processed
the image, options for the user to reduce noise are severely
limited, as the user faces the increased possibility of introducing
additional artifacts in the detail optimization process. Processing
the image a second time for noise will sacrice detail in the
post-capture process, causing the image to develop an even
more unnatural appearance.
The Need for Processing Power
Without question, being able to conveniently and immediately
reduce noise in-camera leads photographers and consumersto use in-camera noise reduction. However a good digital
camera capturing a 4 mega pixel image, for example, may
have only 1/5th of a second to process the image. In this time,
it is conceivable that roughly 50 operations per pixel can be
performed. While it may sound considerable, this is a limitation
that is signicant and restricts the effectiveness of noise
reduction that can take place within the camera. By contrast, a
Digital Raw & JPEG
Some high end digital cameras and
digital SLRs offer options to capture
and store processed les such as JPEGs
while also storing raw, unprocessedles. The benet of doing this is that
the user has an original le that can be
processed later based on the needs for
that image with regard to detail and
noise.
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state of the art computer can easily perform 1,000 times more
operations per pixel, enabling more advanced and dynamic
functionality in the noise reduction process. When combined
with other input variables such as the human eye, effective
noise reduction is better achieved in the post-capture stage.
Undesirable Effects of Reducing Noise
When applied improperly, noise reduction frequently creates a
range of undesirable effects in digital images. These byproducts
of noise reduction have a varying level of impact on an image,
ranging from the introduction of subtle and unnatural detail
changes to the creation of a completely articial-looking digital
image.
From a mathematical standpoint, many types of noise reductionartifacts are identical. However, from a subjective point of view
the various types of artifacting create aberrations that are often
signicant and apparent.
Blind Area Artifacting
Blind Area Artifacting occurs when high detail areas are noise-
reduced ineffectively based on assumptions about details within
a digital image. Detail in a digital image often ranges from
low to very high, depending on the image detail that is being
represented. When noise is effectively reduced in a low detail
area and then applied equally to an area which contains high
detail, important aspects of the image lose detail to the degree
that structure disappears or appears unnatural.
Blind Area Artifacting typically occurs in hair and other
ne detail structures. Hair structures and their details, for
example, while very subtle, are essential to the images natural
appearance. Changing those structures in the noise reduction
process calls unwarranted attention to that detail, resulting in
the unnatural feeling or perception of the image.
The unnatural impactof Blind Area Artifacting
occurs because the human
eye expects certain details
to be present in areas
that no longer have the
necessary detail, creating ablind area of detail.
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Detail, Noise Relationships, and Blind Area Artifacting
Blind Area Artifacting is a byproduct of many non-selective
noise reduction processes that are directly connected to the
relationship of noise-to-detail across a digital image.
In the accompanying illustration, the red line represents the
The mathematical explanation
for Blind Area Artifacting is
relatively straightforward.
Similar to the way that thehuman ear receives sound
logarithmically (exponential
increase of sound appears as a
linear increase of volume to the
listener), optical noise is often
perceived in a similar fashion.
This means, however, that if
there is an image structure in a
certain image area with contrast
that is 25% (one quarter) of the
noise in this area, the human eyewould perceive 100% noise and
50% (one half) existing image
structure, due to the logarithmic
nature of structure perception.
However, assuming that
structure and noise are
somewhat distributed with
Gaussian standard aberration,
the 100% noise and the 25%
image structure do not add
up to 125% structure in the
image. This calculation keeps
in mind that overlaying 100%
of a Gaussian noise structure
with the same amount (100%)
of another structure (be that
another noise source or actual
image detail) will typically
result not in 200% of structure
contrast, but only 141% image
contrast (square root of 2). Of
course, this may also vary based
on the characteristics of both
structures.
In short, when a noise structure
in an image area is perceived
by the human eye with only
slightly more contrast than the
existing image structure in the
same image area, the actual
image detail in this area is
slightly stronger than the detail
in an area where only noise
with no underlying structure
is present. In other words, thenoise swallows image detail
much faster than it appears to
the viewer. When the noise is
reduced, this important detail is
obscured, resulting in Blind Area
Artifacting and unnatural, often
plastic-like structures.
When noise in a structure is
perceived as having slightly
more contrast than the
surrounding context, the
aberrant detail tends to dene
the desirable detail to a degree
which is disproportionate to its
strength in the picture. And as
a result, in reducing the noise,
detail is sacriced.
Contrasting Noise and Detail
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noise intensity in an image (here assumed to be constant
throughout the image), and the blue line represents the
images detail structure, which will vary in different areas of
the image. The blue line goes from a relatively low point to a
relatively high point, as most images have varying degrees of
structural complexity.
At point 1 in the diagram, it can be seen that the detail
structure (blue) has approximately one third less contrast
than the noise level, while at point 2 the image structure is
only about one third stronger than the noise intensity. That
is, in one area within image, such as the sky of an outdoor
photo, noise is visually more dominant than the image details
themselves (represented by point 1 in the illustration). In these
cases, noise swallows the image detail and becomes the
dominant detail. However, in another area of the image, such
as an area consisting of foliage, the images detail structure is
more dominant than the noise (represented by point 2 in the
illustration).
When a non-selective noise reduction process attempts to
reduce these areas to the same degree or to similar extents,
image details that have a noise-to-detail relationship as
indicated in point 1 become the focus of the noise reduction
process and often erase or diminish these details. The result
is the creation of Blind Areas within image details, and since
these details have an important relationship in the image from
a photographic perspective, the noise reduction process creates
problems rather than solves them.
Remaindered Pixels
Remaindered Pixels often appear as single, aberrant pixels that
for one reason or another were not considered by the noise
reduction process, leaving a single noise pixel among dissimilar
detail. Remaindered Pixels are often apparent in areas with lowor plain detail after certain noise reduction routines are used.
Remaindered Pixels can occur as byproducts of a number of
noise reduction methods. They frequently appear as the result
of simplistic noise reduction methods or those that consider and
lter details based on a threshold approach.
The Need For Selectivity
While in some instances noise can be
readily differentiated from detail (high
frequency noise against soft structures
such as clouds, for example) most noise
occurs in the presence of detail in such
a way that selective noise reduction
approaches are necessary to obtain the
desired results.
Deta
ilStr
uctu
re
Noise Intensity
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If, for example, an image area that has 1,000 pixels with a
considerable dispersion of noise, there will typically be a small
percentage of pixels out of this 1,000 with noise strength or
intensity relative to the surrounding pixels that is much higher
than the average noise strength. Many conventional noise
reduction algorithms, typically those that utilize threshold
methods, leave a few pixels in the image unaffected, as the
algorithm assumes that they are relevant details based on a
xed threshold.
The result is randomly remaindered and distributed pixels in
the image that are not considered to be noise, regardless of
their impact on the image. This small number of pixels with the
highest noise intensity in an image will remain unaffected and
can become even more apparent against the low detail areas.
This Remaindered Pixel effect is often most apparent with
digital cameras that use built-in noise reduction. In general,
built-in noise reduction techniques in digital cameras produce
the most artifacts, since the processing capabilities of digital
camera chips are very limited when compared to computers,
and camera chips are not sufcient for processing sophisticated
algorithms.
Remaindered Pixels also occur typically when noise is not
sufciently reduced or when the noise reduction algorithm isnot capable of considering a large enough sample of the image
information. Noise reduction methods that can only compare
a pixel with its directly adjacent pixels (such as most noise
reduction methods inside digital cameras) are less effective in
distinguishing wanted contrast from unwanted contrast, further
contributing to the creation of Remaindered Pixels.
Painterly Effect
The Painterly Effect is a byproduct of noise reduction where
certain details are obscured to a point that it appears similar
to a painted detail. Painterly-looking details appear when
noise reduction methods are used that change the structure of
the image as they perform more complex operations, such as
reducing any pixels contrast by a certain extent.
Typically, more complex noise reduction methods, such as
a Median lter, change the images structure to a point
When a noise reduction method takes only
a small sample from the image, it can take
only few pixels into account, such as 3x3 or5x5 pixel block. These methods frequently
cannot make signicant or profound
considerations whether one pixel is part of
an unwanted noise structure or not. Given a
small block of pixels, these noise reduction
calculations are no more capable than the
human eye of determining whether the
outlined pixel above, for example, is noise
or important detail.
When a noise reduction method uses a
larger block of pixels, as shown above,
more intelligent considerations about the
pixel are possible and the algorithm is not
limited to more simplistic approaches as
those that rely on a threshold concept.
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where the image may not appear in certain areas to be noise
reduced; however, in the process, ne details become altered
to a point where unnatural structures appear.
The Painterly Effect becomes a signicant problem because this
type of artifacting appears to the human eye to be among themost unnatural type of structure. When more complex noise
reduction methods attempt to reconstruct edges or displace
pixels (such as the Median lter) painterly artifacts become
more dominant in higher detail areas. Painterly-looking
artifacting can be found commonly in offset printed images
where a Median lter or a similar method is used to reduce
noise in detailed areas.
Blurring Effect
A Blurring Effect is arguably one of the more common side
effects of noise reduction. When blurring methods are used
as a primary basis for noise reduction, image details become
softened to the same degree as the targeted noise. While all
noise reduction methods reduce image detail to some extent,
the Blurring Effect appears as an unnecessary softening of the
image in the noise reduction process.
The Blurring Effect frequently appears in a noise-reduced
image because of the complex relationship between noise and
image detail and the varying degrees of noise that appear from
image to image. Blurring noise by a factor of X in one image,
for example, will not have the same qualitative impact when
applied to the same degree in another image. When noise
reduction methods use blurring methods to globally reduce
noise, an unnecessary Blurring Effect can result in varying
intensities, depending on the image and its details.
Resolution Issues: Screen Versus Print Images
Print Optimized Versus Screen View PresentationOptimized noise reduction for print must be considered
differently from optimized noise reduction for on-screen
viewing. As the effective resolution in print is often three to
four times as high as the resolution at which an image appears
on the screen, details must be dealt with differently in each
scenario. This is not to say that a print optimized image
cannot be displayed on a CRT, LCD, or plasma display screen.
When noise reduction
methods use blurring
methods to globally reducenoise, an unnecessary
Blurring Effect can result
in varying intensities,
depending on the image
and its details.
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However, as both image presentation methods present image
details, the nal method that will be used for the presentation
of the detail becomes a primary consideration in the noise
reduction process.
Print optimized noise reduction must consider and respect thenature of the noise as detail, since noise is integral to image
detail. The objective is to reduce its appearance to a level
below the threshold of ordinary perception. In our analysis of
noise reduction and detail optimization, we emphasize the nal
presentation of the image as the standard for judging noise,
rather than the noise reduction process itself. In other words, it
is not the algorithm or mathematical process and its ability to
reduce noise that is important; rather, it is the nal condition of
the image in print across a variety of images that we consider.
In doing so, it is the users perception of the detail in the image
that determines the effectiveness of the entire noise reduction
process.
As we consider this, we acknowledge that there are different
needs for noise reduction and detail optimization for screen
viewed images than for the printed image. Because the image
is presented on paper, the effective resolution is greater than it
would be for the same image viewed on a screen. Therefore, it
is important to process the image based on the printed image.
Effective noise reduction should consider the print process,
the manner in which details appear in print, and the higher
resolution that the print process utilizes. By optimizing noise
reduction for print, image detail is considered for the higher
of the two presentation standards, enabling the image to be
effectively optimized and presented via either medium.
The Non-Scientic and Subjective Nature of Perception
Regardless of how good the camera is or how mathematically
perfect the post-capture processing, the creation of a qualityimage depends on a good eye and good subjective judgment.
An optimized system for noise reduction needs to consider the
subjective nature of perception among other dynamic factors in
the noise reduction process.
Visible noise detracts from the photographic qualities of
the image at one level, while noise that appears below the
level of perception detracts at a different level and must be
Screen and Print Resolutions
Resolution and detail presented via a
computer monitor appears only at a
fraction of that of the printed image.
Effective monitor resolution can beapproximated by calculating the
following:
The approximated display resolution of
a 19 inch monitor with a video card/
adapter setting of 1024x768 can be
calculated by dividing the HORIZONTAL
resolution of the video card setting by
the physical HORIZONTAL measurement
of the screen 1024/14.5 = 70.62 dpi.
By optimizing noise
reduction for print, image
detail is considered for
the higher of the two
presentation standards,
enabling the image to
be effectively optimized
and presented via either
medium.
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dealt with differently. This difference needs to be identied
and recognized in developing an effective noise reduction
system from a qualitative (perceptual) and mathematical
(quantitative) perspective. Again, we stress that the manner in
which noise is manifested in the image (how the noise appears)
is a key variable and a fundamental element of effective noise
reduction.
Details and the Power of the Human Eye
Practical & Subjective Issues in Noise Reduction
Photographic detail and the perception of detail in the image
are also key considerations for optimizing detail naturally.
Various noise reduction methods approach noise globally,
providing the user with variables to adjust, resulting in a
range of results. As we discuss in the preceding section, these
methodologies often suffer from a range of side effects that
occur as a result of the dynamic nature of the image and image
detail as well as the limitation of any individual method. It is
important to recognize that there are dynamic aspects of the
digital photograph that cannot be addressed purely by one
single algorithm or method for all images.
Detail structures appear differently from detail type to detail
type. Skin, for example, appears differently than foliage,
and both foliage and skin appear differently than sky detail.
Additionally, image detail appears differently when in proximity
to different colors. These differences are among other
signicant considerations in the noise reduction process, as the
objective is to control the perceptual level of noise reduction
and ensure detail optimization.
An optimal system for detail optimization and noise reduction
involves varying mathematical approaches and varying degrees
of interaction that include these detail and color considerations
while also factoring in the human eye and the tools of imaging
science.
There are occasions when a process can be created to provide
visually acceptable noise reduction in an automated fashion
when appropriate variables and specic image detail are
considered. In these cases, tools can be created to adapt to the
specic needs of the capture process and the images that will
How noise appears in an
image is a key variable and
a fundamental element of
effective noise reduction.
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be captured. But because noise and details differ from image
to image, an optimal approach requires the human eye to
distinguish noise from detail and to distinguish what appears to
be natural.
The challenge of noise reduction is to provide an effectivemethod that can adapt to the dynamics of the image, the
specic details of the image, and ultimately the manner in
which the human eye perceives the noise-to-detail relationship.
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Optimized Noise Reduction
Power, versatility, and control together contribute to an
effective noise reduction and detail optimization solution. To
be effective, a noise reduction system must work in conjunction
with all aspects of the digital image.
However, as powerful as a solution may be, in order to be
effective it must be simple to use and it must empower the
user to be effective in the image editing process. When we
consider the constant changes and advances in digital imaging
technologies, users are left with the daunting task of learning
and staying current with technology.
An effective noise reduction and detail optimization process
provides users with a progressive tool that offers ease-of-
use and power while considering users varying levels of
sophistication. A progressive noise reduction tool offers a
process that enables users to adapt the process to their needs
and empowers them to be effective at their level, while
being able to expand their abilities as they grow with digital
photography and adapt to the latest technologies that are
available.
Dne provides an effective system for noise reduction and
detail optimization through a series of tools that are as
powerful or as simple as the user wants it to be.
Dne provides a system that enables the user to:
1. Preview and analyze the image
2. Optimize an image based on a specic digital camera
3. Reduce random color noise and maintain color details
4. Optimize image details and their relationship to artifacts
5. Selectively reduce noise quickly and intuitively
6. Optimize color changes and their relationship to detail
7. Control light and contrast in image details
Regardless of how noise is manifested in the image, Dne
provides a system for reducing noise with respect to image
detail. Dne utilizes a number of tools that provide options for
reducing noise based on the appearance of noise in the image,
An effective noise
reduction and detail
optimization process
provides users with aprogressive tool that offers
ease-of-use and power
while considering the users
level of sophistication.
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ranging from camera-specic proles to selective application
tools that reduce noise based on the presence of specic detail
types.
1. Previewing and Analyzing the Image
It is important for the user to be able to preview, analyze, and
observe the process of noise reduction and detail optimization.
Viewing details and their relationship to noise is an essential
part of an efcient post-capture noise reduction process.
However, identifying and detecting noise can be one of the
more difcult steps in noise reduction. Lower screen resolutions
(compared to print) and varying types of image detail can
make it difcult to identify and reduce noise while maintaining
the natural photographic appearance of the image.
Auto-Detection of Noise in Sensitive Areas
The Auto-Detection feature in Dne acts as a key tool for
identifying noise. When Dne is opened and the ve-preview
window option is selected, the Auto-Detection feature locates
three areas in the image that contain characteristics that are
typically subject to noise.
The Auto-Detection feature enables users to locate and observe
the noise reduction process in sensitive areas, or to locate other
areas with these characteristics and selectively reduce noise inthe image. The Auto-Detection system is designed to help the
user identify noise in the image, thereby serving as a tool for
analysis as well as helping to educate users by identifying areas
that are susceptible to noise in an image.
Multi-Preview Mode
The multiple preview option in Dne is an important tool
in optimizing detail and treating noise in the image. Most
conventional analysis tools provide zooming previews of a
single area of an image in order to view the changes to the
image in the noise reduction process. Moving zoom options are
also popular methods for analysis of an image in the editing
process.
However, as detail is changed in one characteristic or image
feature, other details are often affected. Observing multiple
details and image characteristics in Dne enables the user to
Preview #1 Locates an area with extensive
highlights. Preview #2 locates an area with
a higher degree of edge detail. Preview #3
locates an area with shadows or low light.
Preview windows #4 and #5 enable before
and after views of the image during the
editing process.
The Dne Preview System
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make adjustments to focal or sensitive areas of the image while
observing their effects on other details.
Analysis Mode: Screen-Based Image Analysis Tools
Analyzing noise and detail in an image on screen is one of
the limitations that makes post-capture noise reduction more
difcult. When reducing noise for a print process, we discuss
the fact that computer screen resolutions and the differences
between display resolution and print resolution are often
impediments to optimizing detail while reducing noise. A
preview zoom feature alone is often insufcient to accurately
and efciently analyze image detail.
The preview analysis tool within Dne plays an important
role within the detail optimization process. Details with specic
characteristics, sensitive areas, and their relationship to the
image as a whole can be viewed to ensure that detail is
maintained and the appropriate amount of noise reduction is
performed.
Grab and Drop Preview
The Grab and Drop preview feature is an important tool
within Dne that makes it easy to perform a balanced image
enhancement or adjustment. The Grab and Drop preview tool
is an efcient image analysis system that enables the userto grab a detail from any preview window and drag it to an
adjacent window. By grabbing and dropping image details, the
user can easily compare sensitive detailin either the normal
or the analysis modeand view the image before and after the
noise removal process.
2. Optimizing Images Based on a Specic Camera
The Unique Nature of Digital Cameras & Image Details
As discussed earlier, all digital cameras capture an image inthe same way, but utilize different calculations to process the
image. That is, each cameras image sensor assembles and
processes the information it captures differently. Aside from
the functional features of the camera, the manner in which
cameras process captured data is the main differentiator from
camera to camera, which necessitates varying noise reduction
approaches for different cameras and a exible tool for
Above: Specic details in the face are
grabbed and dropped from preview #4
(lower left) up to the preview #2 (top
middle). The preview is adjusted to a 100%(1:1) view to observe changes at actual
image size.
Above: The Analysis Mode enables users
to identify details and observe luminosity
changes to avoid blowing out high contrast
details or dropping off low contrast
details.
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dynamic noise reduction in the captured image.
The Detail-to-Color Correlation
Targeted Reduction and the Camera Profile Controller
As previously discussed, noise appears in digital images andbecomes intertwined among varying types and varying levels
of image details. Additionally, noise becomes apparent when
present against different colors and detail structures in a digital
image.
Common solutions for noise reduction typically involve isolating
an individual color channel of an image. The occurrence of
noise as it appears in the different color channels of a digital
image is often discussed when the issue of noise reduction is
addressed. The presence of noise in the blue channel of anRGB image, for example, often leads many users to focus on
the reduction of noise in one particular color channel without
regard to other elements of the image.
When noise is reduced in one channel of an image, the
contrast of edges or the shape of details in only one channel
of the image is changed without regard for the relationship
of the two remaining color channels and their independent or
combined effect on the image. The practice of single-channel
noise reduction treating one third of an image componentwithout regard to related components is utilized in no other
photographic, optical, or similar medium or technology because
the results are not true to the original image.
The more effective implementation of targeting noise is
provided in Dne and involves a proprietary process for
considering detail and color in the noise reduction process.
This system not only allows the user to target noise within a
problem area, but also provides a system of calculations to
balance the photographic relationship of detail in the image.
This becomes important for a variety of reasons. Most
importantly, at the conscious level, we can reduce noise
effectively using the relationship of color and noise at the pixel
level. Rather than solely isolating the color channel, we are
able to identify specic colors (such as the blue color in a sky
scene), their relationship to noise, and their relationship to
Targeting the blue channel
of an RGB image ignores
some of the basic tenets of
photographic relationshipsin the digital image.
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detail across the image. Below the level of perception, noise
plays an important role in the detail structure of the image.
As the user identies colors that need to be treated with noise
reduction, detail structures and their relationship to these
structures can be considered across the image.
To obtain a natural reduction of noise while optimizing wanted
detail, the user is able to identify the targeted area for
reduction while setting the detail-sensitive area to have a low
level of reduction. A balancing of noise and detail will occur
based on the presence of detail and its relationship to color
across the image. As certain colors present noise differently,
and as the human eye discerns colors differently (not all colors
are perceived to the same degree), the image benets from
being treated in a dynamic fashion to achieve a more natural
result. Limitations of noise reduction in specic detail areas can
be identied to provide additional control over the image.
3. Reducing Color Noise and Maintaining Color Details
The reduction of Chrominance Noise (color noise) is frequently
discussed in books, seminars, and classes related to image
editing. The random nature of Chrominance Noise often makes
dealing with this type of noise difcult. Among the difculties is
the need to maintain color details and their transitions to avoid
contributing to a digitally processed appearance. When randomChrominance Noise is reduced inappropriately, it can create
an unnatural appearance in color details. When inappropriate
Chrominance Noise reduction is combined with other similar
image changes, the effect can have an even more negative
impact on the image.
Blurring Lab Channels Versus Dfine Detail Protection
Converting an image to Lab color mode and blurring the
a and b color channel information is a frequently taught
method for Chrominance Noise reduction. However, the impact
of using this technique can vary signicantly across images and
have a negative impact on the image. Similar to blurring the
blue channel of an RGB image, Lab color channel blurring uses
an approach that is utilized in no other photographic, optical,
or similar medium or technology because the results are not
true to the original image.
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[Figure 1]
The color circles in this original illustration show
a distribution of how random chrominance noiseappears against colors in an image.
[Figure 2]
When a and b channel blurring is performed
on an Lab color mode image, image detail is
blurred and color transitions can suffer from a
haloing effect as detail is lost on the color edge
transitions.
[Figure 1a]
An illustration of the distribution of luminosity
(the brightness) of noise in Figure 1. The smallwaves on this luminosity distribution curve
represent noise, with the large transition in
the curve representing a color transition at the
edge of a color circle in Figure 1.
[Figure 2a]Articial color transitions occur when
important image detail is blurred. This global
approach to addressing random noise lacks
any consideration for the image details and
can create an articial appearance in images,
which will vary from image-to-image.
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When color detail is blurred (within the a and b channels) with
the objective of reducing noise, the transition that is created
is softer and edge detail is lost, as shown in Figure 2. When
blurring Lab color channels, the image will contain softer color
transitions in the color channels than in the luminance channel.The result of this blurring can have a variety of impacts on the
image.
When reducing chrominance noise, the protection of color
detail becomes one of the primary considerations. When
Chrominance Noise reduction considers image details,
important color transitions can be controlled and maintained
in the noise reduction process to create unmodied color
transitions, as illustrate