Image enhancement algorithms & techniques Point-wise operations 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)

Transform domain image processing Generalized linear filtering 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  

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.

(grey scale transformation) 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). m 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))

Contrast enhancement/contrast stretching 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 and b, b(2/3)L)  p<1, for the bright regions (above b).

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

Original histogram Processed histogram

v = L-u (6.3) 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)

GREY SCALE SLICING (WINDOWING): (6.4) or (6.5) 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)

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

IMAGE SUBTRACTION: _

  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). u Hlin,U(u) Hlin,V(v) v Ideally – histogram equalization Digital image processing Chapter 6. Image enhancement

Fig. 6.10 The resulting image after histogram equalization 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

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

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) 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)

Directional low-pass spatial 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     a b Fig. 6.14 Additive noise attenuation by mean filtering  

UNSHARP MASKING AND EDGE CRISPENING: 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

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

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

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

Zooming by linear interpolation: (6.36) (6.37) (6.38) (6.39) 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

Fig. 6.24 Image enhancement in the transformed domain 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

E.g.: - the inverse Gaussian filter has the following regional mask: 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     

Fig. 6.28 Color image enhancement block diagram 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

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

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

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

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