Single Image Defogging Algorithm based on Dark Channel Priority

Based on dark channel priority, the paper proposes an improved defogging algorithm of single image which can defog the foggy images rapidly. The algorithm in the paper applies the method combining adaptive median filter and bilateral filter to figure out clear dark channel on the edge. And the algorithm is based on the physical model of foggy images to estimate transmission. Compared with the traditional algorithm, the estimated transmission is detailed and clear, and has no need to be optimized, which not only overcomes the disadvantages of traditional algorithm using plenty of time to optimize transmission, but also reduces the complexity of the algorithm. The experimental results indicate that the algorithm realizes rapid and high-quality defogging on single image

[1]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[2]  Carlo Gatta,et al.  A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Contrast , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

[4]  Michael Unser,et al.  Fast $O(1)$ Bilateral Filtering Using Trigonometric Range Kernels , 2011, IEEE Transactions on Image Processing.

[5]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Wencheng Wang,et al.  Study on the Restoration Method from Motion Blurred Image , 2012 .

[7]  Yoav Y. Schechner,et al.  Blind Haze Separation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[8]  Kunal N. Chaudhury,et al.  Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means , 2012, ArXiv.

[9]  Dani Lischinski,et al.  Deep photo: model-based photograph enhancement and viewing , 2008, SIGGRAPH 2008.

[10]  Jean-Philippe Tarel,et al.  BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES , 2011 .

[11]  Sunav Choudhary,et al.  A Fuzzy Logic Based Approach to De-Weather Fog-Degraded Images , 2009, 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization.

[12]  Ruizhong Rao,et al.  Improved single image dehazing using segmentation , 2010, 2010 IEEE International Conference on Image Processing.

[13]  Xiu-Juan Deng,et al.  A New Kind of Weighted Median Filtering Algorithm Used for Image Processing , 2008, 2008 International Symposium on Information Science and Engineering.

[14]  Peter Shirley,et al.  A practical analytic model for daylight , 1999, SIGGRAPH.

[15]  Kunal N. Chaudhury,et al.  Acceleration of the Shiftable $\mbi{O}{(1)}$ Algorithm for Bilateral Filtering and Nonlocal Means , 2012, IEEE Transactions on Image Processing.

[16]  Frédo Durand,et al.  Light mixture estimation for spatially varying white balance , 2008, ACM Trans. Graph..

[17]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[18]  Ze Wang,et al.  Optimal Stopping Condition for Iterative Deconvolution Method by The Measurement of Image Texture , 2012 .

[19]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Xiaoping Liu,et al.  Research of Image Recognition based on Rough Set , 2012 .

[21]  Zhou Wang,et al.  Translation insensitive image similarity in complex wavelet domain , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[22]  Raanan Fattal Single image dehazing , 2008, SIGGRAPH 2008.