1. Image Deblurring Using Dark Channel Prior
Liang Zhang (lzhang432)

2. Outline Motivation Solutions Application Future Work Reference
Dark Channel
Model Optimization
Application
Future Work
Reference

3. Motivation Recover Blur Image
Photos are taken everyday (mobile phone, digital camera, GoPros)
Blur Images are undesirable
Hard to reproduce the capture moment
How to get deblur image without have to retake picture?
Example of Blur Image

4. Motivation Blur Image = Sharp Image * Blur Kernel + Noise[8]
Clear Image
Blur Kernel
Noise
Blur Image = Sharp Image * Blur Kernel + Noise[8]

5. Motivation Blur Image = Sharp Image * Blur Kernel + Noise[8]
Clear Image
Blur Kernel
Noise
Blur Image = Sharp Image * Blur Kernel + Noise[8]

6. Outline Motivation Solutions Application Future Work Reference
Dark Channel
Model Optimization
Application
Future Work
Reference

7. Convolution Convolution: Weighted average of a patch 50 10 120 30 180
120 30
180 25 90 60 2 20 75 3 80 15 5 12
300
200
150
1/9
Blur Kernel
Clear Image

8. Convolution Convolution: Weighted average of a patch 50 10 120 30 180
120 30
180 25 90 60 2 20 75 3 80 15 5 12
300
200
150
1/9 45
Blur Kernel
Clear Image

9. Convolution Dark Channel: Convolution: >=
The lowest value among a patch
Convolution:
Weighted average of a patch 50 10
120 30
180 25 90 60 2 20 75 3 80 15 5 12
300
200
150
1/9 45
>=
Blur Kernel
Clear Image

10. Convolution and Dark Channel
Image
Dark channel of blurred image are less sparse than the dark channel of sharp image
Dark Channel
Clear Image
Blurred Image

11. Deblur Approach
Image
Dark Channel
Clear Image
Blurred Image

12. Outline Motivation Solutions Application Future Work Reference
Dark Channel
Model Optimization
Application
Future Work
Reference

13. Model
Based of fact that the dark channel of sharp image have more number of zero-intensity pixels
[8]

14. Model
Based of fact that the dark channel of sharp image have more number of zero-intensity pixels
[8]
Data Fitting

15. Model Blur Kernel Regularization
Based of fact that the dark channel of sharp image have more number of zero-intensity pixels
[8]
Blur Kernel Regularization

16. Model Gradients of Image Sparsity
Based of fact that the dark channel of sharp image have more number of zero-intensity pixels
[8]
Gradients of Image Sparsity

17. This term is used to measure the sparsity of dark channel
Model
Based of fact that the dark channel of sharp image have more number of zero-intensity pixels
[8]
L0 norm
Non-linear min operator
This term is used to measure the sparsity of dark channel

18. Non-linear Operation D(I) = MI M D(I): vectorized of D(I)
M: indicator matrix of dark channel
I: vectorized latent image
[8] M
Latent Image
Dark Channel

19. Non-linear Operation D(I) = MI M D(I): vectorized of D(I)
M: indicator matrix of dark channel
I: vectorized latent image
[8] M
Latent Image
Dark Channel

20. Non-linear Operation D(I) = MI M D(I): vectorized of D(I)
M: indicator matrix of dark channel
I: vectorized latent image
[8] M
Latent Image
Dark Channel

21. Blur Kernel and Clear Image
Dark Channel of Latent Image
Blur Kernel
Clear Image

22. Outline Motivation Solutions Application Future Work Reference
Dark Channel
Model Optimization
Application
Future Work
Reference

23. Application
Run with paper’s data set

24. Application
Run with paper’s data set

25. Application
Run with our own data set (blur images are download from google)

26. Outline Motivation Solutions Application Future Work Reference
Dark Channel
Model Optimization
Application
Future Work
Reference

27. Future Work
Parallel computing implementation to accelerate deblur computing
Implement the dark channel on mobile phone
Improve the dark channel methods

28. Outline Motivation Solutions Application Future Work Reference
Dark Channel
Model Optimization
Application
Future Work
Reference

29. Reference
[1]  L.B.Lucy. An iterative technique for the rectification of observed distributions. Astronomy Journal, 79(6): , 1974.
[2]  T, Chan anc C.Wong. Total variation blind deconvolution.. IEEE TIP, 7(3): , 1998.
[3]  R.Fergus, B.Singh, A.Hertzmann, S.T.Roweis, and W.T.Freeman. Removing camera shake from a single photograph. ACM SIGGRAPH, 25(3): , 2006.

30. Reference
[4]  Y.Hacohen, E. Shechtman, and D.Lischinski. Deblurring by example using dense correspondence. In ICCV, pages , 2013
[5]  D.Krishnan, T.Tay, and R.Fergus. Blind deconvolution using a normalized sparsit measure. In CVPR, pages , 2011
[6]  T.Michaeli and M.Irani Blind deblurring using internal patch recurrence. In ECCV, pages , 2014

31. Reference
[7]  K.He, J.Sun, and X.Tang Single image haze removal using dark channel prior. In CVPR, pages , 2009
[8]  Jinshan Pan, Deqing Sun, Hanspeter Pfister, and Ming-Hsuan Yang Blind Image Deblurring Using Dark Channel Prior. In CVPR, 2016
[9]
[10]
[11]

32. Thanks

33. Q & A

34. Backup slides