1. Single Image Haze Removal Using Dark Channel Prior Professor : 王聖智 教授 Student : 戴玉書 CVPR 2009. Best Paper AwardBest Paper Award Kaiming HeKaiming He, Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China Jian Sun Xiaoou TangXiaoou Tang, Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China

2. Outline  Background  What is the Dark Channel Prior?  How to estimate ?  How to estimate atmospheric light?  Estimating the transmission t(x) & Soft Matting  Recovering the Scene Radiance  Result

3. Background  Observed intensity  Scene radiance  The global atmospheric light  The medium transmission,

4. Outline  Background  What is the Dark Channel Prior?  How to estimate light?  How to estimate atmospheric light?  Estimating the transmission t(x) & Soft Matting  Recovering the Scene Radiance  Result

5. Dark Channel Prior Observation on haze-free outdoor images: Observation on haze-free outdoor images: In most of the non-sky patches, at least one color channel has very low intensity at some pixels In most of the non-sky patches, at least one color channel has very low intensity at some pixels

6. Mainly due to three factors  Shadows  Colorful of objects or surfaces  Dark objects

7. haze-free imageThe dark channel of haze-free image

9. Statistics of the dark channels Except for the sky region, the intensity of is low and tends to be zero Except for the sky region, the intensity of is low and tends to be zero

10. Visually, the intensity of the dark channel is rough approximation of the thickness of the haze haze image The dark channel of haze image

11. Outline  Background  What is the Dark Channel Prior?  To estimate of light  To estimate of atmospheric light  Estimating the transmission t(x) & Soft Matting  Recovering the Scene Radiance  Result

12. To estimate of To estimate of atmospheric light  Pick the top 0.1% brightest pixels in the dark channel

13. Outline  Background  What is the Dark Channel Prior?  How to estimate light?  How to estimate atmospheric light?  Estimating the transmission t(x) & Soft Matting  Recovering the Scene Radiance  Result

14. Estimating the transmission          

17. Soft Matting Image matting equation:

19.  L ij :   Minimize the following cost function: A. Levin, D. Lischinski, and Y. Weiss. A closed form solution to natural image matting. CVPR, 1:61–68, 2006. 4, 5, 7

22. Outline  Background  What is the Dark Channel Prior?  How to estimate light?  How to estimate atmospheric light?  Estimating the transmission t(x) & Soft Matting  Recovering the Scene Radiance  Result

23.  (t 0 =0.1)

24. Outline  Background  What is the Dark Channel Prior?  How to estimate light?  How to estimate atmospheric light?  Estimating the transmission t(x) & Soft Matting  Recovering the Scene Radiance  Result

25. Result  The patch size is set to 15x15  Soft matting: Preconditioned Conjugate Gradient (PCG) algorithm  Local min operator using Marcel van Herk ’ s fast algorithm fast algorithm

31. ► Tan's result ► Fattal's result ► Dark channel

33. ► Tan's result ► Fattal's result ► Dark channel

35. ► Kopf et al's result ► Dark channel