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. 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 Digital image processing Chapter 6. Image enhancement

3. 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. Digital image processing Chapter 6. Image enhancement

4. 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). Digital image processing Chapter 6. Image enhancement mm nn U[M×N]U[M×N]V[M×N]V[M×N] Point-wise operation (grey scale transformation) f(∙) => v=f(u) u(m,n)u(m,n) v(m,n) = f(u(m,n))

5. 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 and b, b  (2/3)L)  p<1, for the bright regions (above b). Digital image processing Chapter 6. Image enhancement

6. 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 Digital image processing Chapter 6. Image enhancement

7. Original histogram Processed histogram

8. 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) Digital image processing Chapter 6. Image enhancement

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

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

11. IMAGE SUBTRACTION: _

12. Digital image processing Chapter 6. Image enhancement HISTOGRAM MODELING. HISTOGRAM EQUALIZATION/MODIFICATION Def. Linear grey level histogram of a digital grey scale image U[M×N]: = the function H lin,U :{0,1,…,L Max }→{0,1,…,MN}, H lin,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 h lin,U :{0,1,…,L Max }→[0;1], h lin,U (u)=H lin,U (u)/(MN). Def. Cumulative grey level histogram of a digital grey scale image U[M×N]: = the function H cum,U :{0,1,…,L Max }→{0,1,…,MN}, Def. Normalized cumulative grey level histogram of the image U[M×N]: = the function h cum,U :{0,1,…,L Max }→[0;1], h cum,U (u)=H cum,U (u)/(MN). u H lin,U (u) H lin,V (v) v Ideally – histogram equalization

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

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

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

16. 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 Spatial averaging. Low-pass spatial filtering: (6.18) (6.19 ) Filtering by spatial averaging – the effect on the noise power reduction: (6.21) (6.22) Digital image processing Chapter 6. Image enhancement

17. 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 a b Fig. 6.14 Additive noise attenuation by mean filtering Digital image processing Chapter 6. Image enhancement

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

19. 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 Digital image processing Chapter 6. Image enhancement

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) Fig. 6.20 Band-pass image filtering BAND-PASS SPATIAL FILTERING: (6.28) Digital image processing Chapter 6. Image enhancement

21. INVERSE CONTRAST RATIO MAPPING; STATISTICAL SCALING: (6.29) (6.30) (6.31) (6.32) (6.33) 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,... Digital image processing Chapter 6. Image enhancement

22. 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 Digital image processing Chapter 6. Image enhancement

23. 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 Digital image processing Chapter 6. Image enhancement

24. 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 Digital image processing Chapter 6. Image enhancement

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

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

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

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

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