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Single Image Haze Removal Using Dark Channel Prior

Single Image Haze Removal Using Dark Channel Prior. CVPR 2009 . Best Paper Award Kaiming He , Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China Jian Sun Xiaoou Tang , Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China.

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Single Image Haze Removal Using Dark Channel Prior

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

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

  5. Dark Channel Prior 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

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

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

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

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

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

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

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

  13. Estimating the transmission

  14. Soft Matting Image matting equation:

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

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

  17. (t0=0.1)

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

  19. 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

  20. Tan's result • Fattal's result • Dark channel

  21. Fattal's result • Tan's result • Dark channel

  22. Kopf et al's result • Dark channel

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