BASICS OF DIGITAL IMAGE PROCESSINGErkki Rämö

Digital image processing

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Editing and interpreting of picture information Examples: Improving the visual quality of the

image Removing an error from the image Automated interpretation of the

image

Related disciplines

Group discussion 1• Discuss application areas of digital image processing.

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Where is image processing applied?

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Biological research – cell studies

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Military research – interpretation of reconnaissance photos

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Document control – scanning, interpretation, archiving

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Industry automation – machine vision

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Forensics – Fingerprint analysis

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Medicine – x-ray image analysis

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Photography – Digital photography

Publishing

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Space investigation

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Remote Sensing

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Mapping (eg. Google street view)

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Film industry

Visual image

Light = electromagnetic radiationDifferent wavelengths of light reflect from the object and absorb to the object in different ways, depending on objects surfaces construction and material

Reflecting light is perceived with the eye-brain visual system as an image

Wavelength of visual light is 400 - 700 nm

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Perceiving of the visual image

What is needed:Light source

Light bulb radiates light of some color

Targetwhich reflects part of the light and absorbs the rest

Eye receives the signal signal is interpreted by brain

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10-6 10 103 109

Cosmicrays

Gammarays

X-rays

UVInfra-red

Micro-wave

Radio

nm

Visible light

400 nm 700 nm

Spectrum of light

Group discussion2• List imaging applications working in different wavelengths.• Can you find imaging using else than electromagnetic

radiation

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Eyesight

Eye, visual nervetrack and brains visual centre form the human visual system

There’s no visual system better than the eye Some animal eyes are better than human

eye Examples of ‘analog’ image processing

A paddle in the water, refraction of light in the interface of two substances

Image restoration by eyeglasses

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From optical image to a digital image

The construction of the eye

Cross-section of the human eye

Comparison between an eye and a cameraSimilarities:

In the eye image is drawn upside down to the retinaPupil works like the iris of the cameraRetina, with two types of visual cells, rods (about 120

million) and cones(about 7 million)

Differencies:Focus by changing the refraction of the lense by means

of the radial deformation

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Visual cells of the eye

Rodsthousands of times more sensitive than cones. responsible of dark vision

ConesResponsible of seeing the colors Three kinds: sensitive for blue-purple, green and red-

yellow. Peaks of sensitivity are in the wavelengths of 447 nm,

540 nm ja 577 nm

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Anatomy of the eyeIn the area of accurate sight, in the middle of the yellow-

spots fovea there are no rods but plenty of cones.Outside the fovea, accuracy of vision is poor

5° from the fovea – only half of the accuracyOnly a small area of field of vision is seen accurateMoving the eyeball we can focus on different details

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Anatomy of the eye 2

Sensitivity of visual cells to alteration of lighting is logarithmicWebers law

JND=K*I Where K is constant and JND (Just Noticable Difference)

Example: 100 W lighting 10 W power increment.

In 1000W lighting we need 100W increment for same resultImage: Intensity must be doubled to notice the same

visual difference

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Visual cells react with one anotherMach Band Effect

Eye works like a high pass filter sharpening the detailsOn the edge of the tone slope, dark color seems lighter and

light color seems darker

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Influence of the background Simultaneous contrast

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Influence of the background Simultaneous contrast

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Frequency responseHow small details are still visible?Influences:

Number and positioning of cells, elasticity of the eye, brain response, intensity of light

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Procedure classes of image processing

Procedures have been developed already in1960’s, though due to lack of computing power they were hard to implement

Some procedures enhance the quality of the imageOthers pick and analyze information from the image

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5 Procedure classes

1. Image Enhancement

2. Image Restoration

3. Image Analysis

4. Image Compression

5. Image Synthesis

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Image Enhancement

Most common procedure classCan be used as independent enhancement method or

as pre-operation for other methods, for example reducing the image before analysis

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Image enhancement 2

Goal is to enhance the visual quality of the imagecontrast and brightnessnoise reductionsharpening

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Example: adjusting contrast

Photoshop ”autolevels”, which implements the whole tone scale for the image

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Image Restoration

Goal is to restore an image as original or removal of known photographic errorCorrections:

• Removal of geometric distortion• Removal of blur• Noise removal• Motion-blur removal

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Example: enhancing sharpnessPhotoshop ”Unsharp mask”

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Image AnalysisAs result there rarely is an image, but information about

what’s in the imageImplemented in various tasks involving artificial vision

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Example: Measuring of an object

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Image compressionGoal is to compress image-information so that it would consume less space

Pros needs less space faster transfer

Methods:lossless compression(max 2:1)lossy compression(max 100:1)

Based on redundant information in the image

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Example: JPEG-compression

183 KB 17 KB

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Image SynthesisImage is built out of other images orVisualization of non-image informationUsed when:

taking a picture is not physically possible fast and/or slow events

modelling an object which does not exist

Examples:2D images of projection images mathematicallyvisualization of chart information as an image

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Construction of image processing application

Application can be divided into unit tasks

• Application construction:

Applications

Fundamental Classes

Operations

Process

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Basic description of application Example application:

Capture video image of cars licence plate Process and interpret the signs on the plate Check register if the vehicle has any offense

Image processing part

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Process image and identify letters and numbers as an array

In short: Read the signs of the licence plate

Process classes

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Divide application into unit tasks Image enhancing: Improve the image

quality Image analysis: Interpret the letters and

numbers of the plate

ZHO-408

Operations48

Image enhancement: Improve the image quality Contrast alteration: steepen the contrast Edge highlighting: Outlines of signs

Image analysis: Interpretation of the letters and numbers of the plate Detaching edges: Follow the outlines Classification of objects: Fit vectors into images in

model library

Methods

Contrast alteration: steepening of contrastContrast stretching as pixel operation

Edge highlighting: Outlines of symbolsSobels edge highlighting algorithm

Finding edges: Follow the outlines Edge finding algorithm

Classification of vectors: Fit vectors into images in model library

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