Digital image processing Chapter 6. Image enhancement

1. Digital image processing Chapter 6. Image enhancement IMAGE ENHANCEMENT Introduction Image enhancement algorithms & techniques Point-wise operations Contrast enhancement; contrast stretching Grey scale clipping; image binarization (thresholding) Image inversion (negative) Grey scale slicing Bit extraction Contrast compression Image subtraction Histogram modeling: histogram equalization/ modification Spatial operations Spatial low-pass filtering Unsharp masking and crispening Spatial high-pass and band-pass filtering Inverse contrast ratio mapping and statistical scaling Magnification and interpolation (image zooming)

2. Digital image processing Chapter 6. Image enhancement Transform domain image processing Generalized linear filtering Non-linear filtering Generalized cepstrum and homomorphic filtering Image pseudo-coloring Color image enhancement Applications: biomedical image enhancement Types and characteristics of biomedical images Contour detection in biomedical images Anatomic segmentation of biomedical images Histogram equalization and pseudo-coloring in biomedical images

3. Digital image processing Chapter 6. Image enhancement • Introduction • Def.: Image enhancement = class of image processing operations whose goal is to produce an output digital image that is visually more suitable as appearance for its visual examination by a human observer • The relevant features for the examination task are enhanced • The irrelevant features for the examination task are removed/reduced • Specific to image enhancement: - input = digital image (grey scale or color) - output = digital image (grey scale or color) • Examples of image enhancement operations: • noise removal; • geometric distortion correction; • edge enhancement; • contrast enhancement; • image zooming; • image subtraction; • pseudo-coloring. • Classification of image enhancement operations: • Based on the type of the algorithms: grey scale transformations; spatial operations; transform domain processing; pseudo-coloring • Based on the class of applications – as in the examples above.

4. m m n n U[M×N] V[M×N] Point-wise operation (grey scale transformation) f(∙) => v=f(u) u(m,n) v(m,n) = f(u(m,n)) Digital image processing Chapter 6. Image enhancement A. Point-wise operations Def.: The new grey level (color) value in a spatial location (m,n) in the resulting image depends only on the grey level (color) in the same spatial location (m,n) in the original image => “point-wise” operation, or grey scale transformation (for grey scale images).

5. Digital image processing Chapter 6. Image enhancement Contrast enhancement/contrast stretching Contrast enhancement, if:  m<1, for the dark regions (under aL/3). n>1, for the medium grey scale (between a andb, b(2/3)L) p<1, for the bright regions (above b).

6. Digital image processing Chapter 6. Image enhancement • Grey scale clipping; image thresholding • Grey scale clipping is a particular case of contrast enhancement, for m=p=0: • (6.2) Fig. 6.3. Grey scale clipping Fig. 6.4 Image thresholding

7. Processed histogram Original histogram

8. Digital image processing Chapter 6. Image enhancement Fig. 6.5 Image thresholding - example The inverse image (negative image): v = L-u (6.3) Fig. 6.6 Image inverting Fig. 6.7 Grey scale slicing (windowing)

9. Digital image processing Chapter 6. Image enhancement GREY SCALE SLICING (WINDOWING): (6.4) or (6.5) BIT EXTRACTION: u=k12B-1+k22B-2+...+kB-12+kB (6.6) (6.7) CONTRAST COMPRESSION: v = clog(1+|u|)(6.8)

10. CONTRAST COMPRESSION – EXAMPLE: v = clog(1+|u|)

11. IMAGE SUBTRACTION: _

12. HISTOGRAM MODELING. HISTOGRAM EQUALIZATION/MODIFICATION Def. Linear grey level histogram of a digital grey scale image U[M×N]: = the function Hlin,U:{0,1,…,LMax}→{0,1,…,MN}, Hlin,U(u)=nbr. of pixels with grey level u from U. Def. Normalized linear grey level histogram of the image U[M×N]: = the function hlin,U:{0,1,…,LMax}→[0;1], hlin,U(u)=Hlin,U(u)/(MN). Def. Cumulative grey level histogram of a digital grey scale image U[M×N]: = the function Hcum,U:{0,1,…,LMax}→{0,1,…,MN}, Def. Normalized cumulative grey level histogram of the image U[M×N]: = the function hcum,U:{0,1,…,LMax}→[0;1], hcum,U(u)=Hcum,U(u)/(MN). Hlin,U(u) u Ideally – histogram equalization Hlin,V(v) v Digital image processing Chapter 6. Image enhancement

13. Digital image processing Chapter 6. Image enhancement Fig. 6.8. Histogram equalization a b Fig. 6.9 Low contrast image a b Fig. 6.10 The resulting image after histogram equalization

14. (6.15) (6.15.a) Digital image processing Chapter 6. Image enhancement Fig. 6.11 Histogram modification

15. - Convolution mask AM Digital image processing Chapter 6. Image enhancement SPATIAL OPERATIONS: most of them can be implemented by convolution

16. Digital image processing Chapter 6. Image enhancement Spatial averaging. Low-pass spatial filtering: (6.18) (6.19) v(m,n)=1/2[y(m,n)+1/4{y(m-1,n)+y(m+1,n)+y(m,n-1)+y(m,n+1)}](6.20) Fig. 6.12 Convolution windows used in low-pass spatial filtering - examples Filtering by spatial averaging – the effect on the noise power reduction: (6.21) (6.22)

17. a b Fig. 6.14 Additive noise attenuation by mean filtering Digital image processing Chapter 6. Image enhancement Directional low-pass spatial filtering: (6.23) Fig. 6.13 Directional spatial filtering Median filtering: (6.24)  v(m,n) = the element in the middle of the brightness row, with increasing brightness values

18. a b Fig. 6.15 Gaussian noise reduction by median filtering UNSHARP MASKING AND EDGE CRISPENING: Digital image processing Chapter 6. Image enhancement (6.25) (6.26) a b c d Fig. 6.16 Edge crispening algorithm

19. Digital image processing Chapter 6. Image enhancement Original image Resulting image Fig. 6.17 Edge crispening using a Laplacian operator HIGH-PASS SPATIAL FILTERING (6.27) • Fig. 6.18 Low-pass filtering Fig. 6.19 High-pass filtering

20. a b c d Fig. 6.21 The results of LPF (Fig. c), HPF (Fig. b),BPF (Fig. d) for a grey level image (Fig. a – original image) Digital image processing Chapter 6. Image enhancement BAND-PASS SPATIAL FILTERING: (6.28) • Fig. 6.20 Band-pass image filtering

21. INVERSE CONTRAST RATIO MAPPING; STATISTICAL SCALING: (6.29) (6.30) (6.31) (6.32) (6.33) Digital image processing Chapter 6. Image enhancement • MAGNIFICATION AND INTERPOLATION (IMAGE ZOOMING): • Zooming by pixel replication: • (6.34) • The resulting image is obtained as: • (6.35) • with m,n =0, 1, 2,...

22. Digital image processing Chapter 6. Image enhancement a b c Fig. 6.22 Image zooming by pixel replication by a factor of: b) 2; c) 4, on each direction Zooming by linear interpolation: (6.36) (6.37) (6.38) (6.39) (6.40) Fig. 6.23

23. Digital image processing Chapter 6. Image enhancement • 6.6 TRANSFORM DOMAIN IMAGE PROCESSING • Generalized linear filtering • (6.41) • where g(k,l) is called regional mask (i.e., it is 0 outside the selected region) Fig. 6.24 Image enhancement in the transformed domain a b Fig. 6.25 Regional masks for the generalized linear filtering

24. Digital image processing Chapter 6. Image enhancement E.g.: - the inverse Gaussian filter has the following regional mask: (6.42) - for other orthogonal transforms: (6.43) Non-linear filtering (6.44) (6.45) Generalized cepstrum and homomorphic filtering

25. Digital image processing Chapter 6. Image enhancement IMAGE PSEUDO-COLORING Fig. 6.27 Monochrome image pseudo-coloring COLOR IMAGE ENHANCEMENT Fig. 6.28 Color image enhancement block diagram

26. Digital image processing Chapter 6. Image enhancement BIOMEDICAL IMAGE ENHANCEMENT - APPLICATIONS Biomedical image types & features Fig. 6.42 Fig. 6.43 Fig. 6.44 Fig. 6.45

27. Digital image processing Chapter 6. Image enhancement Contour extraction in biomedical images: Table 6.1 (6.76) Fig. 6.46 Fig. 6.47

28. Digital image processing Chapter 6. Image enhancement Histogram equalization and pseudo-coloring in biomedical images: a b Fig. 6.48 Fig. 6.49 Fig. 6.50

29. Digital image processing Chapter 6. Image enhancement Fig. 6.51 Fig. 6.52 Fig. 6.53 Fig. 6.54