1. 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)

2. 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

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. (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))

5. 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).

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 Grey scale clipping
Fig Image thresholding

7. Original histogram
Processed histogram

8. 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 Image inverting Fig Grey scale slicing (windowing)

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

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

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

15. 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 Convolution windows used in low-pass spatial filtering - examples
Filtering by spatial averaging – the effect on the noise power reduction: 
(6.21)
(6.22)

16. Directional low-pass spatial filtering:
Digital image processing Chapter 6. Image enhancement
Directional low-pass spatial filtering:
(6.23)
Fig 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 Additive noise attenuation by mean filtering

17. UNSHARP MASKING AND EDGE CRISPENING:
a b
Fig 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 Edge crispening algorithm

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

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

20. 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,...

21. Zooming by linear interpolation: (6.36) (6.37) (6.38) (6.39)
Digital image processing Chapter 6. Image enhancement
a b c
Fig 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

22. 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 Image enhancement in the transformed domain
a b
Fig Regional masks for the generalized linear filtering

23. 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   

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

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

26. 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 Fig. 6.47

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

28. Digital image processing Chapter 6. Image enhancement
Fig Fig. 6.52   
Fig Fig. 6.54