1. Digital Image Processing In The Name Of God Digital Image Processing Lecture6: Color Image Processing M. Ghelich Oghli By: M. Ghelich Oghli E-mail: m.g31_mesu@yahoo.com Fall 2012

2. Visible Spectrum

3. Lighting conditions Image taken lit by a flash. Image taken lit by a tungsten lamp. The lighting conditions of the scene have a large effect on the colours recorded.

4. Color Representation

5. Indexed Images

6. Color characteristic  Hue : An attribute associated with the dominant Wavelength( or dominant color perceived by an observer)  Saturation: refers to relative purity or amount of white light mixed with hue( colors such as pink (red and white) are less saturated)  Hue & Saturation --> chromaticity  Brightness: chromatic notion of intensity

7. Color Models  RGB Color Model (space or system): each color appears in its primary components of red green and blue.

8. RGB Color Space

9. CMY and CMYK Color Model  Cyan Magenta and yellow color (secondary colors) is used to represent colors  Used mostly in printing devices  for better printing of black color, this color may be added to the system (CMYK system)

10. CMYK Model  Converting CMYK to RGB  Converting RGB to CMYK( semi-equivalent(

11. HSI Color Model

14. Color Transformation

15. YCbCr (used in MPEG video) Other Color Spaces YUV( used in PAL TV systems)

16. Color Image Processing Full color processing: the images in question typically are acquired with full color sensor Pseudo-color processing: assigning a color to a particular monochrome intensity or range of intensities.

17. Pseudocolor Image processing  Also Called false Image processing  Definition: is the process of assigning color to monochrome image  Advantage: Human can discern thousands of colors compared to one or two dozen shades of gray.  Method: Intensity Slicing

18. Intensity Slicing

19.  Single level slicing( X-ray Weld image)

20. Intensity Slicing  Multi level slicing( radiation image)

21. Intensity Slicing

22. Gray Level to color Transformation

23. Basics of Full color Image Processing Per-color-component based processing: each color is processed individually and then is combined to obtain processed image Vector based processing: each color pixel is interpreted as vector and processed individually.

24. Basics of Full color Image Processing

25. Color Transformation  Gray level transformation  Color transformation

26. Color Transformation

27. Color Complement

29. Tone and Color Correction  Tone Correction: adjusting brightness and contrast of the image experimentally  methods 1- changing I in HSI model 2- mapping all colors of RGB or CMYK space with the same transformation

30. Tone correction

31. Color Correction

32. Color Image Smoothing

36. Color Image Laplacian