This paper presents an automatic method for the defogging from a single haze image. To recover a foggy image, an accurate depth map is estimated from a multi-level inference method, which fuses depth maps with different sizes of patches by dark channel prior. Markov random field (MRF) is applied to label the depth level in adjacent region for the compensation of wrong estimated regions. Airlight is automatically estimated as the deepest and largest area from the MRF labeled result. The accurate estimation of airlight provides good restoration with respect to visibility and contrast but without oversaturating. The algorithm is verified by a handful of foggy and hazy images. Experimental results demonstrate that the defogging method can recover high-quality images through accurate estimation of depth map and airlight.



Results and Depth maps

input foggy image	our result	our depth map
input foggy image	our result	our depth map
input foggy image	our result	our depth map
input foggy image	our result	our depth map

More Results

input foggy image	our result
input foggy image	our result
input foggy image	our result
input foggy image	our result
input foggy image	our result

Iterative defogging

input foggy image	first iteration result	second iteration result
third iteration result	fourth iteration result	fifth iteration result

Comparisons:

input foggy image	He's result [1]	Kopf's result [2]
Tan's result [3]	Fattal's result [4]	our result
input foggy image	He's result [1]	Kopf's result [2]
Tan's result [3]	Fattal's result [4]	our result

 

 1. ICGIP 2012 presentation slides

 2. CVGIP 2012 presentation slides

 3. Experimental images

                                                                        Reference

[1] Y. K. Wang, C. T. Fan, and C. W. Chang, "Accurate Depth Estimation for Image Defogging using Markov Random Field," in 4th International Conference on Graphic and Image Processing, Singapore, 2012

[2] Y. K. Wang, C. T. Fan, and C. W. Chang, "Single Image Defogging by Multi-level Depth Estimation and Automatic Airlight Extraction," The 25th IPPR Conference on Computer Vision, Graphics, and Image Processing, Taiwan, 2012

[3] K.He, J. Sun, and X. Tang, "Single Image Haze Removal using Dark Channel Prior," in CVPR, 2009.

[4] J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, "Deep Photo: Mo

del-Based Photograph Enhancement and Viewing," ACM Transactions on Graphics, 2008.

[5] R. T. Tan, "Visibility in Bad Weather from a Single Image," in CVPR, 2008.

[6] R. Fattal, "Single Image Dehazing," ACM Transactions on Graphics, 2008.