1. image enhancement using MATLAB Digital Image Processing 2014 Fall NTU 1

2. Outline Spatial domain – gray-level transformation – histogram processing – linear filter – non-linear filter Frequency domain – Gaussian low-pass filter (smoothing filter) – Gaussian high-pass filter (sharpening filter) – high-frequency emphasis filtering 2

3. Outline Spatial domain – gray-level transformation gamma transformation image negatives contrast stretching log transformation – histogram processing – linear filter – non-linear filter Frequency domain 3

4. gray-level transformation gray-level transformation is a pixel by pixel operation to change the contrast of an overall image 4

5. gamma transformation g = imadjust(f, [low_in high_in], [low_out high_out], gamma); 5

6. gamma transformation g = imadjust(f, [0.5 0.75], [0 1]); 6

7. gamma transformation g = imadjust(f, [0.5 0.75], [0 1]); 7

8. gamma transformation g = imadjust(f, [], [], 2); 8

9. gamma transformation g = imadjust(f, [], [], 2); 9

10. image negatives g = imadjust(f, [0 1], [1 0]); g = imcomplement(f); 10

11. contrast stretching Low_High = stretchlim(f); g = imadjust(f, Low_High, []); 11

12. contrast stretching Low_High = stretchlim(f); g = imadjust(f, Low_High, [1 0]); 12

13. contrast stretching Low_High = stretchlim(f); g = imadjust(f, Low_High, [1 0]); 13

14. log transformation g = log(1 + double(f)); 14

15. Outline Spatial domain – gray-level transformation – histogram processing histogram equalization histogram matching contrast-limited adaptive histogram equalization – linear filter – non-linear filter Frequency domain 15

16. histogram equalization g = histeq(f, 256); 16

17. histogram equalization g = histeq(f, 256); 17

18. histogram matching p = twomodegauss(0.15, 0.05, 0.75, 0.05, 1, 0.07, 0.002); g = histeq(f, p); 18

19. histogram matching p = twomodegauss(0.15, 0.05, 0.75, 0.05, 1, 0.07, 0.002); g = histeq(f, p); 19

20. contrast-limited adaptive histogram equalization g = adapthisteq(f); 20

21. contrast-limited adaptive histogram equalization g = adapthisteq(f, 'NumTiles', [25 25]); 21

22. contrast-limited adaptive histogram equalization g = adapthisteq(f, 'NumTiles', [25 25], 'ClipLimit', 0.05); 22

23. Outline Spatial domain – gray-level transformation – histogram processing – linear filter mean filter Laplacian filter – non-linear filter Frequency domain 23

24. mean filter mean filter is windowed filter of linear class, that smoothes image 24

25. Laplacian filter f2 = tofloat(f); g2 = imfilter(f2, w, 'replicate'); 25

26. Laplacian filter Image enhanced using the Laplacian filter 26

27. Outline Spatial domain – gray-level transformation – histogram processing – linear filter – non-linear filter median filter Frequency domain 27

28. median filter salt and pepper noise ： – fn = imnoise(f, 'salt & pepper', 0.2); 28

29. median filter gm = medfilt2(fn); 29

30. median filter gms = medfilt2(fn, 'symmetric'); 30

31. Outline Spatial domain – gray-level transformation – histogram processing – linear filtering – non-linear filtering Frequency domain – Gaussian low-pass filter (smoothing filter) – Gaussian high-pass filter (sharpening filter) – high-frequency emphasis filtering 31

32. Gaussian low-pass filter F = fft2(f); H = lpfilter('gaussian', M, N, sig); G = H.* F; g = ifft2(G); 32

33. Outline Spatial domain – gray-level transformation – histogram processing – linear filtering – non-linear filtering Frequency domain – Gaussian low-pass filter (smoothing filter) – Gaussian high-pass filter (sharpening filter) – high-frequency emphasis filtering 33

34. Gaussian high-pass filter H = hpfilter('gaussian', PQ(1), PQ(2), D0); g = dftfilt(f, H); 34

35. Outline Spatial domain – gray-level transformation – histogram processing – linear filtering – non-linear filtering Frequency domain – Gaussian low-pass filter (smoothing filter) – Gaussian high-pass filter (sharpening filter) – high-frequency emphasis filtering 35

36. high-frequency emphasis filtering HBW = hpfilter('btw', PQ(1), PQ(2), D0, 2); gbw = dftfilt(f, HBW, 'fltpoint'); gbw = gscale(gbw); 36

37. high-frequency emphasis filtering H = 0.5 + 2*HBW; ghf = dftfilt(f, H, 'fltpoint'); ghf = gscale(ghf); 37

38. high-frequency emphasis filtering ghe = histeq(ghf); figure, imshow(ghe); 38