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- 1. EECE/CS 253 Image Processing Richard Alan Peters II Department of Electrical Engineering and Computer Science Fall Semester 2007 Lecture Notes: Introduction and Overview 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.
- 2. Introduction and Overview This presentation is an overview of some of the ideas and techniques to be covered during the course.
- 3. <ul><li>1. image formation </li></ul><ul><li>2. point processing and equalization </li></ul><ul><li>3. color correction </li></ul><ul><li>4. the fourier transform </li></ul><ul><li>5. convolution </li></ul><ul><li>6. image sampling and warping </li></ul><ul><li>7. spatial filtering </li></ul><ul><li>8. noise reduction </li></ul><ul><li>9. mathematical morphology </li></ul><ul><li>10. image compression </li></ul>Topics
- 4. Wallace and Gromit Wallace and Gromit will be subjects of some of the imagery in this introduction. Wallace Gromit likes cheese reads Electronics for Dogs http://www.aardman.com/wallaceandgromit/index.shtml Visit:
- 5. Image Formation object image plane lens
- 6. Image Formation light source
- 7. Image Formation projection through lens image of object
- 8. Image Formation projection onto discrete sensor array. digital camera
- 9. Image Formation sensors register average color. sampled image
- 10. Image Formation continuous colors, discrete locations. discrete real-valued image
- 11. Digital Image Formation: Quantization continuous color input discrete color output continuous colors mapped to a finite, discrete set of colors.
- 12. Sampling and Quantization pixel grid sampled real image quantized sampled & quantized
- 13. Digital Image a grid of squares, each of which contains a single color each square is called a pixel (for picture element ) Color images have 3 values per pixel; monochrome images have 1 value per pixel.
- 14. Color Images <ul><li>Are constructed from three intensity maps. </li></ul><ul><li>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. </li></ul><ul><li>The intensity maps are overlaid to create a color image. </li></ul><ul><li>Each pixel in a color image is a three element vector. </li></ul>
- 15. Color Images On a CRT
- 16. Point Processing original + gamma - gamma + brightness - brightness original + contrast - contrast histogram EQ histogram mod
- 17. Color Processing requires some knowledge of how we see colors
- 18. Eye’s Light Sensors #(blue) << #(red) < #(green) cone density near fovea
- 19. 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.
- 20. 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.
- 21. Color Sensing / Color Perception luminance hue saturation photo receptors brain The eye has 3 types of photoreceptors: sensitive to red, green, or blue light. The brain transforms RGB into separate brightness and color channels ( e.g. , LHS).
- 22. Color Perception all bands luminance chrominance red green blue 16 × pixelization of: luminance and chrominance (hue+saturation) are perceived with different resolutions, as are red, green and blue.
- 23. Color Perception all bands luminance chrominance red green blue 16 × pixelization of:
- 24. Color Balance and Saturation Uniform changes in color components result in change of tint. E.g., if all G pixel values are multiplied by > 1 then the image takes a green cast.
- 25. Color Transformations Image aging: a transformation, , that mapped:
- 26. The 2D Fourier Transform of a Digital Image Let I ( r,c ) be a single-band (intensity) digital image with R rows and C columns. Then, I ( r,c ) has Fourier representation where are the R x C Fourier coefficients. these complex exponentials are 2D sinusoids.
- 27. 2D Sinusoids: ... are plane waves with grayscale amplitudes, periods in terms of lengths, ... A = phase shift r c orientation
- 28. 2D Sinusoids: ... specific orientations, and phase shifts. orientation r c r c
- 29. The Value of a Fourier Coefficient … … is a complex number with a real part and an imaginary part. If you represent that number as a magnitude, A , and a phase, , … ..these represent the amplitude and offset of the sinusoid with frequency and direction .
- 30. The Sinusoid from the Fourier Coeff. at ( u , v )
- 31. The Fourier Transform of an Image I | F { I } | [ F { I }] magnitude phase
- 32. Continuous Fourier Transform The continuous Fourier transform assumes a continuous image exists in a finite region of an infinite plane. The BoingBoing Bloggers
- 33. Discrete Fourier Transform The discrete Fourier transform assumes a digital image exists on a closed surface, a torus. The BoingBoing Bloggers
- 34. Convolution Sum times 1/5 Sums of shifted and weighted copies of images or Fourier transforms.
- 35. Convolution Property of the Fourier Transform The Fourier Transform of a product equals the convolution of the Fourier Transforms. Similarly, the Fourier Transform of a convolution is the product of the Fourier Transforms
- 36. Sampling, Aliasing, & Frequency Convolution aliasing (the jaggies) no aliasing (smooth lines)
- 37. Sampling, Aliasing, & Frequency Convolution (a) (b) (c) (d) <ul><li>a) aliased </li></ul><ul><li>b) power spectrum </li></ul><ul><li>c) unaliased </li></ul><ul><li>d) power spectrum </li></ul>
- 38. Resampling 8 × 16 × nearest neighbor nearest neighbor bicubic interpolation bicubic interpolation (resizing)
- 39. Rotation and motion blur
- 40. Image Warping
- 41. Frequency Domain (FD) Filtering Gaussian LPF in FD Original Image Power Spectrum Image size: 512x512 SD filter sigma = 8
- 42. FD Filtering: Lowpass Original Image Filtered Image Filtered Power Spectrum Image size: 512x512 SD filter sigma = 8
- 43. FD Filtering: Highpass Original Image Filtered Image Filtered Power Spectrum Image size: 512x512 FD notch sigma = 8
- 44. FD Filtering: Highpass Original Image Filtered Image Filtered Power Spectrum Image size: 512x512 FD notch sigma = 8 signed image with 0 at middle gray
- 45. Spatial Filtering original blurred sharpened
- 46. Spatial Filtering bandpass filter unsharp masking original
- 47. Spatial Filtering bandpass filter unsharp masking original signed image with 0 at middle gray
- 48. Motion Blur vertical regional zoom rotational original
- 49. Noise Reduction color noise blurred image color-only blur
- 50. Noise Reduction 5x5 Wiener filter color noise blurred image
- 51. Noise Reduction original periodic noise frequency tuned filter
- 52. Shot Noise or Salt & Pepper Noise + shot noise - shot noise s&p noise
- 53. Nonlinear Filters: the Median s&p noise original median filter
- 54. Nonlinear Filters: Min and Maxmin + shot noise min filter maxmin filter
- 55. Nonlinear Filters: Max and Minmax - shot noise max filter minmax
- 56. Nonlinear Processing: Binary Morphology Cross-hatched pixels are indeterminate. “ L” shaped SE O marks origin Foreground: white pixels Background: black pixels
- 57. <ul><li>Used after opening to grow back pieces of the original image that are connected to the opening. </li></ul><ul><li>Permits the removal of small regions that are disjoint from larger objects without distorting the small features of the large objects. </li></ul>Nonlinear Processing: Binary Reconstruction original opened reconstructed
- 58. Nonlinear Processing: Grayscale Morphology Cross-hatched pixels are indeterminate. “ L” shaped SE O marks origin Foreground: white pixels Background: black pixels
- 59. Grayscale Morphology: Opening opening: erosion then dilation opened & original
- 60. Grayscale Morphology: Opening erosion & opening erosion & opening & original
- 61. Nonlinear Processing: Grayscale Reconstruction reconstructed opening original
- 62. Forensic Analysis of Photographs Photographs by Robert Fenton of a battlefield in the Crimean war taken on 23 April 1855. From Morris, Errol, “Which Came First, the Chicken or the Egg?”, Parts 1-3, New York Times , Zoom Editorial Section , 25 Sept. 2007 (pt.1), 7 Oct. 2007 (pt.2), 30 Oct. 2007 (pt.3). Which came first?
- 63. Forensic Analysis of Photographs Photographs by Robert Fenton of a battlefield in the Crimean war taken on 23 April 1855. From Morris, Errol, “Which Came First, the Chicken or the Egg?”, Parts 1-3, New York Times , Zoom Editorial Section , 25 Sept. 2007 (pt.1), 7 Oct. 2007 (pt.2), 30 Oct. 2007 (pt.3). Which came first?
- 64. Forensic Analysis of Photographs Photographs by Robert Fenton of a battlefield in the Crimean war taken on 23 April 1855. From Morris, Errol, “Which Came First, the Chicken or the Egg?”, Parts 1-3, New York Times , Zoom Editorial Section , 25 Sept. 2007 (pt.1), 7 Oct. 2007 (pt.2), 30 Oct. 2007 (pt.3). Which came first?
- 65. Image Compression Yoyogi Park, Tokyo, October 1999. Photo by Alan Peters. Original image is 5244w x 4716h @ 1200 ppi: 127MBytes
- 66. Image Compression: JPEG JPEG quality level File size in bytes
- 67. Image Compression: JPEG JPEG quality level File size in bytes
- 68. Image Compositing <ul><li>Combine parts from separate images to form a new image. </li></ul><ul><li>It’s difficult to do well. </li></ul><ul><li>Requires relative positions, orientations, and scales to be correct. </li></ul><ul><li>Lighting of objects must be consistent within the separate images. </li></ul><ul><li>Brightness, contrast, color balance, and saturation must match. </li></ul><ul><li>Noise color, amplitude, and patterns must be seamless. </li></ul>
- 69. Image Compositing Example Prof. Peters in his home office. Needs a better shirt.
- 70. Image Compositing Example This shirt demands a monogram.
- 71. Image Compositing Example He needs some more color.
- 72. Image Compositing Example Nice. Now for the way he’d wear his hair if he had any.
- 73. Image Compositing Example He can’t stay in the office like this.
- 74. Image Compositing Example Where’s a hepcat Daddy-O like this belong?
- 75. Image Compositing Example In the studio! Collar this jive, Jackson. Like crazy , Man !

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