Digital Image Processing

1. This work is licensed under the Creative Commons Attribution-Noncommercial 2.5 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/2.5/ or send a letter to Creative Commons, 543 Howard Street, 5th Floor, San Francisco, California, 94105, USA. Digital Image Processing Lecture Notes: Introduction and Overview Dr. Chung-Hao Chen Department of Electrical and Computer Engineering Old Dominion University Courtesy of Dr. Richard Alan Peters II Department of Electrical Engineering and Computer Science Vanderbilt University

2. Introduction and Overview This presentation is an overview of some of the ideas and techniques to be covered during the course. 1999-2011 by Richard Alan Peters II

3. Topics 1. Image formation 2. Color correction 3. Image sampling, warping, and stitching 4. Spatial filtering 5. Noise reduction 6. High dynamic range imaging 7. Image compression 1999-2011 by Richard Alan Peters II

4. lens object image plane Image Formation 1999-2011 by Richard Alan Peters II

5. light source Image Formation 1999-2011 by Richard Alan Peters II

6. Image Formation projection through lens image of object 1999-2011 by Richard Alan Peters II

7. Image Formation projection onto discrete sensor array. digital camera 1999-2011 by Richard Alan Peters II

8. Image Formation sensors register average color. sampled image 1999-2011 by Richard Alan Peters II

9. Image Formation continuous colors, discrete locations. discrete real-valued image 1999-2011 by Richard Alan Peters II

10. Digital Image Formation: Quantization discrete color output continuous colors mapped to a finite, discrete set of colors. continuous color input 1999-2011 by Richard Alan Peters II

11. Digital Image Color images have 3 values per pixel; monochrome images have 1 value per pixel. a grid of squares, each of which contains a single color each square is called a pixel (for picture element) 1999-2011 by Richard Alan Peters II

12. Are constructed from three intensity maps. Each intensity map is pro-jected through a color filter (e.g., red, green, or blue, or cyan, magenta, or yellow) to create a monochrome image. The intensity maps are overlaid to create a color image. Each pixel in a color image is a three element vector. Color Images 1999-2011 by Richard Alan Peters II

13. Color Processing requires some knowledge of how we see colors 1999-2011 by Richard Alan Peters II

14. cone density near fovea #(blue) << #(red) < #(green) Eye’s Light Sensors 1999-2011 by Richard Alan Peters II

15. Color Sensing / Color Perception These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye. 1999-2011 by Richard Alan Peters II

16. Color Sensing / Color Perception These are approximations of the responses to the visible spectrum of the “red”, “green”, and “blue” receptors of a typical human eye. The simultaneous red + blue response causes us to perceive a continuous range of hues on a circle. No hue is greater than or less than any other hue. 1999-2011 by Richard Alan Peters II

17. Color Sensing / Color Perception The eye has 3 types of photoreceptors: sensitive to red, green, or blue light. luminance hue saturation The brain transforms RGB into separate brightness and color channels (e.g., LHS). brain photo receptors 1999-2011 by Richard Alan Peters II

18. luminance and chrominance (hue+saturation) are perceived with different resolutions, as are red, green and blue. 16×pixelization of: Color Perception chrominance luminance all bands blue green red 1999-2011 by Richard Alan Peters II

19. Rotation and motion blur 1999-2011 by Richard Alan Peters II

20. Image Warping 1999-2011 by Richard Alan Peters II

21. Panorama via Overlay Originals Bruno Postle http://hugin.sourceforge.net/tutorials/two-photos/en.shtml Merged* 1999-2011 by Richard Alan Peters II *not so good.

22. Panorama via Stitching Originals Bruno Postle http://hugin.sourceforge.net/tutorials/two-photos/en.shtml Merged* 1999-2011 by Richard Alan Peters II *much better.

23. Spatial Filtering blurred original sharpened 1999-2011 by Richard Alan Peters II

24. Spatial Filtering bandpass filter unsharp masking original 1999-2011 by Richard Alan Peters II

25. Spatial Filtering signed image with 0 at middle gray bandpass filter unsharp masking original 1999-2011 by Richard Alan Peters II

26. Motion Blur regional vertical original rotational zoom 1999-2011 by Richard Alan Peters II

27. blurred image color-only blur color noise Noise Reduction 1999-2011 by Richard Alan Peters II

28. blurred image color noise Noise Reduction 5x5 Wiener filter 1999-2011 by Richard Alan Peters II

29. Noise Reduction periodic noise frequency tuned filter original 1999-2011 by Richard Alan Peters II

30. + shot noise s&p noise - shot noise Shot Noise or Salt & Pepper Noise 1999-2011 by Richard Alan Peters II

31. original s&p noise median filter Nonlinear Filters: the Median 1999-2011 by Richard Alan Peters II

32. + shot noise min filter maxmin filter Nonlinear Filters: Min and Maxmin 1999-2011 by Richard Alan Peters II

33. Nonlinear Filters: Max and Minmax - shot noise max filter minmax 1999-2011 by Richard Alan Peters II

34. High Dynamic Range (HDR) Imaging Bartlomiej Okonek http://www.easyhdr.com/examples.php under exposed 1999-2011 by Richard Alan Peters II

35. High Dynamic Range (HDR) Imaging Bartlomiej Okonek http://www.easyhdr.com/examples.php default exposure 1999-2011 by Richard Alan Peters II

36. High Dynamic Range (HDR) Imaging Bartlomiej Okonek http://www.easyhdr.com/examples.php over exposed 1999-2011 by Richard Alan Peters II

37. High Dynamic Range (HDR) Imaging Bartlomiej Okonek http://www.easyhdr.com/examples.php combined 1999-2011 by Richard Alan Peters II

38. Image Compression Original image is 5244w x 4716h @ 1200 ppi: 127MBytes Yoyogi Park, Tokyo, October 1999. Photo by Alan Peters. 1999-2011 by Richard Alan Peters II

39. Image Compression: JPEG File size in bytes JPEG quality level 1999-2011 by Richard Alan Peters II

40. Image Compression: JPEG File size in bytes JPEG quality level 1999-2011 by Richard Alan Peters II

41. Image Compositing • Combine parts from separate images to form a new image. • It’s difficult to do well. • Requires relative positions, orientations, and scales to be correct. • Lighting of objects must be consistent within the separate images. • Brightness, contrast, color balance, and saturation must match. • Noise color, amplitude, and patterns must be seamless. 1999-2011 by Richard Alan Peters II

42. Image Compositing Example Prof. Peters in his home office. Needs a better shirt. 1999-2011 by Richard Alan Peters II

43. Image Compositing Example This shirt demands a monogram. 1999-2011 by Richard Alan Peters II

44. Image Compositing Example He needs some more color. 1999-2011 by Richard Alan Peters II

45. Image Compositing Example Nice. Now for the way he’d wear his hair if he had any. 1999-2011 by Richard Alan Peters II

46. Image Compositing Example He can’t stay in the office like this. 1999-2011 by Richard Alan Peters II

47. Image Compositing Example Where’s a hepcat Daddy-O like this belong? 1999-2011 by Richard Alan Peters II

48. Image Compositing Example Collar this jive, Jackson. Like crazy, Man ! In the studio! 1999-2011 by Richard Alan Peters II