1. A Hybrid Strategy for Improving Illumination-Balance of Degraded Text-Photo Images
Chair Professor Chin-Chen Chang
Feng Chia University
National Chung Cheng University
National Tsing Hua University

2. Outline Introduction The proposed scheme Experimental results
Conclusions

3. Introduction (1/5)
Digital archive

4. Introduction (2/5)
Uneven Illumination distribution

5. Introduction (3/5) Text image balance scheme (Degraded Image)
(Processed Image)

6. Introduction (4/5)
Text-photo image?
(Text-photo Image)

7. Introduction (5/5) The proposed scheme (Degraded Image)
(Processed Image)

8. Flowchart (Photo Part) (Balanced) (Degraded Image) (Processed Image)
(Text Part)
(Balanced)

9. Four Phases of Proposed Scheme
Edge Detection Phase
Object Classification Phase
Text Balance Phase
Photo Balance Phase

10. Edge Detection Phase (1/5)
Sobel edge detection -1 1 -2 2 -2 -1 1 2 -1 -2 1 2 1 2 -1 -2
(Detector (1). 0°)
(Detector (2). 45°)
(Detector (3). 90°)
(Detector (4). 135°)

11. Edge Detection Phase (2/5)
Ex. 1: Edge area 20 10 90 -1 1 -2 2
(Detector (1). 0°)
(Original Image)
(20×-1) + (10×0) + (90×1) +
(20×-2) + (10×0) + (90×2) +
(20×-1) + (10×0) + (90×1) = 280

12. Edge Detection Phase (3/5)
Ex. 2: Smooth area 20 10 -1 1 -2 2
(Detector (1). 0°)
(Original Image)
(20×-1) + (10×0) + (10×1) +
(20×-2) + (10×0) + (10×2) +
(20×-1) + (10×0) + (10×1) = 40

13. Edge Detection Phase (4/5)
Average edge image
Edge
Image 0°
Edge
Image 45°
Edge
Image 90°
Edge
Image 135° + + + = 4
(Average Edge Image)
Edge map
＞ Threshold  edge (255)
≦ Threshold  non-edge (0) 
(Average Edge Image)

14. Edge Detection Phase (5/5)
Ex: threshold = 55 10 80 20 90 60 70 30
255
(Average Edge Image)
(Edge Map)
(Edge Map)

15. Object Classification Phase (1/3)
Object detection
Object 1
255 10
Object 2
Object 3
(Edge Map)

16. Object Classification Phase (2/3)
Mark square areas
(Edge Map)
(Objects)

17. Object Classification Phase (3/3)
Classification according to object size
(Text Part)
(Photo Part)

18. Text Balance Phase (1/4) Flowchart (Processed Text Image)
(Light Distribution Image)

19. Text Balance Phase (2/4) Compute background Ex: 100 text 200 100 120
LIT: The light distribution image for text part
N: The total number of pixels
Ex:
100
text
200
100
120
140
160
180
200
(Text Part) .
(Text Objects)

20. Text Balance Phase (3/4) Illumination Balance Ex: bf = 230
(LIT)
(Original Image)
(Balanced Image)
Ex: bf = 230
bf : The bright factor
Oi: The ith pixel of original image
BI : The balanced image

21. Text Balance Phase (4/4) Enhance contrast Ex: C1=30, C2=1.2
(Original)
(Enhanced)
Ex: 80 60 96 72 40
120 60 12
108 36
BI : Balanced image
C1, C2 : Contrast parameters
C1=30, C2=1.2
(40 – 30) × 1.2 = 12
(120 – 30) × 1.2 = 108
(60 – 30) × 1.2 = 36

22. Photo Balance Phase (1/4)
Purpose
(Degraded Image)
(Processed)

23. Photo Balance Phase (2/4)
Enhance brightness
Ex: pvi= 53, pvi is in Case3
 pvi= pvi+ 25= 78
pvi : The ith pixel value
(Enhanced)

24. Photo Balance Phase (3/4)
Enhance the contrast of each photo object
A dynamic histogram equalization scheme (2007)
(Object 1)
(Object 2)
(Object 1 processed)
(Object 2 processed)

25. Photo Balance Phase (4/4)
Ex: histogram equalization
Contrast Enhancement
(Object 1)
(Object 1 processed)
(Original histogram)
(Contrast enhanced)

26. Final Phase
(Balanced)
(Processed Image)
(Balanced)

27. Experimental Results (1/7)
Scanned Images
Artificial Scanned-Liked Image
512×512 pixels
VLB: S.C. Hsia and P.S. Tsai (2005)
LLB: S.C. Hsia, M.H. Chen and Y.M. Chen (2006)

28. Experimental Results (2/7)
Scanned text-photo images 1 2
The degraded image
VLB
LLB
The proposed scheme 3 4

29. Experimental Results (3/7)
Scanned text images 1 2
The degraded image
VLB
LLB
The proposed scheme 3 4

30. Experimental Results (4/7)
Scanned-liked images
(Original Image)
(Scanned-liked Image)

31. Experimental Results (5/7)
Scanned-liked text-photo images 1 2
The Scanned-liked Image
VLB
LLB
The proposed scheme 3 4

32. Experimental Results (6/7)
Scanned-liked text images 1 2
The Scanned-liked Image
VLB
LLB
The proposed scheme 3 4

33. Experimental Results (7/7)
Compute PSNR values
Scanned-liked text images
Scanned-liked text-photo image

34. Conclusions Practical for text and text-photo images
Satisfactory image quality