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Simple Image Processing

Simple Image Processing. Speaker : Lin Hsiu-Ting Date : 2005 / 04 / 27. Outline. Concept of Image Processing Space Domain Image Processing Frequency Domain Image Processing Geometry Transform Shape Processing Color System. Concept of Image Processing. Concept. The “Image”

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Simple Image Processing

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  1. Simple Image Processing Speaker : Lin Hsiu-Ting Date : 2005 / 04 / 27

  2. Outline • Concept of Image Processing • Space Domain Image Processing • Frequency Domain Image Processing • Geometry Transform • Shape Processing • Color System

  3. Concept of Image Processing

  4. Concept • The “Image” • Signals We Can See Include Special Information • We Process These Signals To Get Relative Information • Integration Technology • Engineering Mathematics • Physical • Biology • Medical Science • Entertainments

  5. Concept • Application • Digital Photo • Map • Natural Disaster Monitored • Others… • Relative Software • Photo Shop • Photo Impact • Others… • These Aren’t Today Key Points

  6. Concept • General Topics of Image Process • Image Capture & Image Digitize • Image Stretch & Remove Distortion • Shape Process • Image Features Extracted • Color Image Process • Image Coding & Compression

  7. Concept • Image Digitized • Sampling • Quantization • Coding • Non-Ideal Situations In Process • Quantization Error • Distortion • Noise

  8. Image with Noise • Images Usually Suffer Noise When Sampling (Like Use Scanners or Digital Cameras…) • Some Common Noise • Dot Noise • Uniform Noise • Sinusoid Wave Noise • Gaussian Noise • Other • Sometimes We Can Remove Noise According Their Features

  9. Image with Noise • Dot Noise • Uniform Noise

  10. Image with Noise • Sinusoid Wave Noise • GaussianNoise

  11. Space Domain Image Processing

  12. Space Domain Image Processing • Characteristic Representation • Profile • Histogram • Statistic ( Mean & Standard Deviation ) • Point Operation • Binarization • Inverse • Contract Stretch • Histogram Equalization • Gamma Correction • Arithmetic & Logic Operation

  13. Binarization • Before Binarization ( 8-bit Gray Level ) • Binarization (Threshold = 200)

  14. Before Processing After Processing Process Flow Load Image Histogram Statistic Stretch Contract Stretch

  15. Before Processing After Processing Process Flow Load Image Histogram Statistic Equalization Histogram Equalization

  16. Image #1 Image #2 Arithmetic (Add & Sub) • Image #1 + Image #2 • Image #1 - Image #2

  17. Space Domain Image Processing • RangeOperation • Smoothing ( Low Pass Filter ) • Median Filter • High Pass Filter • Differentiation • Mask Matrix Note ：We Can Also Use 5x5 , 7x7 or Larger Matrix Process Range Operation But It Cause More Computing

  18. Before Processing After Processing For Every 3 x 3 Block Search Cn = Median (C) Let f (x , y) = Cn Note : The Method Will Have Poor Result When A Lot Of Noise Cluster Median Filter

  19. Frequency Domain Image Processing

  20. Frequency Domain Image Processing • Fast Fourier Transform • Implement • Recursion Algorithm • Butterfly Algorithm • Easy To Achieve Filter • High Pass / Low Pass • Band Pass / Notch

  21. Frequency Domain Image Processing • 2D Fast Fourier Transform Do FFT For Every Row Do FFT For Every Column . . . . . . . . . . . . . . . . . . …………….. F ( u , v ) Note : We Always Use Log Unit Present The Spectrum DistributeInstead of Linear Because Its Dynamic Range is Larger Then Screen

  22. Image Spectrum Image with Sin Noise Spectrum Frequency Domain Image Processing

  23. Geometry Transform

  24. Geometry Transform • Coordinates Transform • Rotation • Scaling • Twist • Gray Level Interpolation • Replicative Interpolation • Bilinear Interpolation

  25. Coordinates Transform • Rotation • Scaling • Twist

  26. Gray Level Interpolation • When We Transform From R to R* • Some Point In R* Can’t Correspond From R • Rotation, Magnify Suffer This Question • Ex: Magnify

  27. Gray Level Interpolation • Replicative Interpolation • Use The Nearest Point To Present • Let j = Int(x+0.5) , k = Int(y+0.5) => g ( x’ , y’ ) = f ( j , k ) • Bilinear Interpolation • Use Four Neighborhood Points • More Smooth Than Replicative

  28. Gray Level Interpolation • Replicative Interpolation • Bilinear Interpolation

  29. Shape Processing

  30. Shape Processing • Find The Edges And Bones • Binarization • Process The Edge And Bone • Erosion • Dilation • Open / Close • Remove Isolate Points • Usually Simple Logic Operation

  31. Binarization Image Erosion Dilation Erosion & Dilation

  32. Color System

  33. Color System • The Colors We See • Wave Length 380 nm ~ 780 nm • Use Rods to Recognize Brightness • Use Cones to Recognize Colors (Three Types For R. G. B. Colors) • Usually Eyes Are More Sensitive To Brightness Than Colors • This Feature is Convenient For Image Compressing

  34. Color System • Common Color System • R. G. B. System (Red, Green and Blue) • C. M. Y. System (Cyan, Magenta and Yellow)-- A Complement of R. G. B • Y. U. V System • Y. I. Q System • H. S. I. System

  35. Conclusion • Image Processing Is Useful • Image Processing Is Interesting • Although We Needn’t Know The Details Of Techniques Because Many Powerful Software Will Handle Them… • But Knowing General Concept Is Helpful For Us

  36. Reference • 數位影像處理 - 連國珍 著, 儒林出版 • http://www.cs.ecnu.edu.cn/teach/down/dip/Chapter02.pps • http://www.fosu.edu.cn

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