1. Digital Image Processing, Spring 2006 1 ECES 682 Digital Image Processing Week 7 Oleh Tretiak ECE Department Drexel University

2. Digital Image Processing, Spring 20062 Announcements Midterm has been graded  Grades posted on webct  Average Select your project! Project due on May 15 Final exam on Monday, June 12

3. Digital Image Processing, Spring 20063 Image Distortion Model Restoration depends on distortion  Common model: convolve plus noise  Special case: noise alone (no convolution)

4. Digital Image Processing, Spring 20064 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

5. Digital Image Processing, Spring 20065 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

6. Digital Image Processing, Spring 20066 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...

7. Digital Image Processing, Spring 20067 Spectral Response of Cones

8. Digital Image Processing, Spring 20068 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

9. Digital Image Processing, Spring 20069 Tristimulus Values

10. Digital Image Processing, Spring 200610 Chromaticity Diagram From http://www.efg2.com/Lab/Graphics/Colors/Chromaticity.htm http://www.efg2.com/Lab/Graphics/Colors/Chromaticity.htm White is (1/3, 1/3, 1/3) Y is the subjective intensity.

11. Digital Image Processing, Spring 200611 Standard Observer

12. Digital Image Processing, Spring 200612 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

13. Digital Image Processing, Spring 200613 RGB Coordinates With the advent of color television, the RGB coordinates were introduced to conveniently produce color on a color cinescope (cathode ray tube)

14. Digital Image Processing, Spring 200614 RGB Gamut Color space produced by a specific set of phosphors or LCD filters is limited

15. Digital Image Processing, Spring 200615 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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