digital image processing, gonzalez & woods 1 digital image processing stmik mdp
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Digital Image Processing, Gonzalez & Woods
1
Digital Image Processing
STMIK MDP
Digital Image Processing, Gonzalez & Woods 2
Digital Image Processing, Gonzalez & Woods 3
Image Distortion Model
• Restoration depends on distortion Common model: convolve plus noise Special case: noise alone (no convolution)
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Thoughts on Restoration
• Enhancement vs restoration Enhancement: cosmetic Restoration: substantive In reality, there’s a continuum
• Restoration relies on prior knowledge• Example of restoration
Inverse filtering Noise removal Nonlinear processing
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Chapter 6, Color Image Processing
• Color fundamentals and models• Pseudocolor
Slicing False-color maps Index color Multispectral color models
• Color transformations• Smoothing and sharpening• Color segmentation
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Retinal Physiology and Color
• Human retinas have (at least) four types of photoreceptors Three types of ‘cones’
High light level, high acuity vision Each type of cone has a different spectral response
One type of ‘rods’ Low-light level and peripheral vision
• There is substantive genetic diversity in color receptors Different spectral response of photoreceptor Absence of one of the pigments Many more phenomena...
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Spectral Response of Cones
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Color Matching Theory
• Young’s observation Any uniform color can be matched by projecting three
different light sources onto a screen• In the 1920’s, an international effort on the part
of a number of physicists let to the CIE standard color theory
• The effort attempted to simplify and standardize• There have been many refinements, but the basic
theory still stands• Theory useful for matching pigments (all paint
stores have spectrophotometers) and design of color media systems
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Tristimulus Values
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
I() — spectal energy distribution of a source
X,Y ,Z — tristimulus values of the source
X x ()I()d400
700
Y y()I()d
400
700
Z z ()I()d
400
700
Chromaticities - trichromatic coefficients
x X
X Y Z
y Y
X Y Z
z Z
X Y Zx y z 1
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Chromaticity Diagram
• From http://www.efg2.com/Lab/Graphics/Colors/Chromaticity.htm
• White is (1/3, 1/3, 1/3)• Y is the subjective
intensity.
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Standard Observer
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Better Subjective Color
• Constant distances in x, y space do not correspond to ‘constant’ changes of color
• A better linear space is provided by the Y-U-V coordinates
Digital Image Processing, Gonzalez & Woods 13
RGB Coordinates
• With the advent of color television, the RGB coordinates were introduced to conveniently produce color on a color cinescope (cathode ray tube)
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RGB Gamut
• Color space produced by a specific set of phosphors or LCD filters is limited
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Constant-Color-Difference Coordinates
• For modulation (analog TV) and encoding (digital TV, JPEG), Y-U-V-like coordinates are produced
• R’, G’, B’ are gamma corrected RGB. These are used in electronic media.
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing
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Chapter 6Color Image Processing
Chapter 6Color Image Processing