ese558 digital image processing
DESCRIPTION
slides for introductory graduate course or senior undergard course in image processingTRANSCRIPT
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Digital Image ProcessingSpring 2007
Sankalp Kallakuri
[email protected] refererenced –
Digital Image Processing by Gonzalez and Woods
Fundamentals of Digital Image Processing by A K Jain
Digital Picture Processing By Rosenfeld and Kak
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Syllabus
• Fundamentals
• Image Enhancement [spatial]
• Image Enhancement [frequency]
• Sampling and Quantization
• Image Restoration
• Color Image Processing
• Image Compression
• Image Reconstruction
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Syllabus
• Grading:
Assignments - 40%
Homework - 10%
Mid Term - 20%
Final - 30%
• Assignments:
Matlab and C/C++
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IP 101
• Colour images
• Grey level images
• File formats JPG BMP TIFF
• 2D representations
• Examples of Fields that use IP
X-Rays, UV Imaging, IR Imaging, Satellite Images, Astronomy, License plates, Water Marking, Microwaves, MRI, sonograms, TEMs
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Image Processing System
Image Displays Processors Mass storage
Hard CopySpecialized IP
Hardware
IP software
Image Sensors
network
Problem domain
From Gonzalez and Woods
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Human Eye
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Vision Details
• Lens Iris Pupil Cornea Retina
• Rods / Cones [distribution number use]
• Blind spot
• Photopic[bright]/ Scotopic[dim]
• Brightness adaptation
• Weber Ratio
Ic
I
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Examples of Brightness perception
Figures from Gonzalez and Woods
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Light and EM Spectrum
• Wavelength = C/ frequency
• Energy = h * frequency
• Reflected light
• Radiance is total amount of energy that flows
from the light source
• Luminance is the perceived from light source
• Sensor design
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Image Sensing and Acquisition
• Single , Line and Array
• Array Strips
• Linear , circular
Bayer and RGB Filter type CCDS
From wikipedia
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Projection
• Perspective
• Orthographic
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Image Model
• f(x,y)
• 0 < f(x,y) <
• f(x,y)=i(x,y)r(x,y)
• i - illuminance r- reflectance
• 0 < i(x,y) <
• 0 < r(x,y) < 1
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Sampling and Quantization
• In 1 dimension
• In 2 dimension
• Effects of quantisation
• Colour levels and bit requirements
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Signals
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sampling
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Quantization
levels
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Sampled
&
Quantized
signal
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Continuous phenomenon
Two orthogonal sine waves
added to each other Continuos Image
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Sampled and Quantised in 1 Dimension
Quantized and sampled Effects are contour lines
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Sampled and Quantised
Contour lines appear on both X and Y dimensions
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Bit Requirements
• L = 2 K
• b= M x N x K
• Example:
100 distinct colors needed to capture a
phenomenon. How many bits would be
needed to store an image of dimensions
49x10?
3430
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Resolution
• Easier to change number of Pixels rather than
number of grey levels.
• Optimal number to use is until there is no
discernible difference by increasing the number.
• Isopreference Curves : curves on the N k plane
• More detail fewer grey levels.
• The higher grey levels will mean better contrast
perception.
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Aliasing
0 frfl
0 Fs-Fs
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Zooming and Interpolation
• Simple zoom would leave blank spaces in the grid.
• Nearest neighbor interpolation.
• Repetition of pixels [integer zoom]
• Bilinear Interpolation
v(x,y)=ax+by+cxy+d
• Shrinking done by removal of columns and rows.
• In case of non integer shrink factor the grid Is zoomed out. Interpolation is performed and then rows and columns are stripped out.
• Smoothing is useful before shrinking.
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Relationships between Pixels
• Neighborhood N4(p) N8(p) ND(p)
• 4 adjacency ,8 adjacency and m adjacency
• Digital path
• Connected Components
• Connected Set [region]
• Border
• Edge [may be local ]
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Distance Measures
• For Pixels p,q and z with coordinates (x,y) (s,t) and (v,w)
• D(p,q) > 0 (D(p,q)=0 iff p=q)
• D(p,q) = D(q,p)
• D(p,z) < D(p,q) + D(q,z)
• City block distance
D4(p,q) = |x-s| + |y-t|
• Chessboard Distance
D8(p,q)=max(|x-s| + |y-t|)
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Home Work & Assignment
• Label all images
• Scripts should be commented.
• A read me file should be attached.
• Assignments shall be incremental.
• So try and complete them by the deadlines.
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Homework -1
• Learn how to read and write an image in matlab.
• Learn basic syntax in Matlab.
• Create a 256x256 2D array. Populate every row with a sine wave which rides on a DC level of 128 with Peak-Peak amplitude 220 , which has exactly two cycles fit in a row.
• display this array as an image.
• Create a 256x256 2D array. Populate every column with a sine wave which rides on a DC level of 10, with Peak-Peak amplitude 20, which has exactly 4 cycles fit in a column.
• Add the two arrays
• Display the sum array as an image