Single Image Dehazing with Lab Analysis

Images acquired by visual framework are genuinely corrupted under cloudy and foggy climate, therefore affecting detection, tracking and recognition of images. Thus, restoring the true scene from a hazy image is of great significance. To solve this problem, this paper presents a real-time effective dehazing algorithm for hazy surveillance images. This algorithm is based on Histogram and a filtering manipulation on La*b* color channel. In the proposed algorithm, the input RGB night image is transformed into La*b* color channel then, contrast limited adaptive histogram equalization(CLAHE) and a smoothing operation is applied respectively and simultaneously on the luminosity layer" L" and the two-color channels (a* and b*) of the La*b* color space. The channels are merged back to obtain a new enhanced image, which is transformed back to RGB image. Experimental results show the effectiveness and the short computational time of the proposed algorithm

[1]  Qi Zhang,et al.  100+ Times Faster Weighted Median Filter (WMF) , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Ko Nishino,et al.  Factorizing Scene Albedo and Depth from a Single Foggy Image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  K K Tan,et al.  Physics-based approach to color image enhancement in poor visibility conditions. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  Khumanthem Manglem Singh,et al.  A Switching vector median filter for impulse noise removal from color images , 2017, TENCON 2017 - 2017 IEEE Region 10 Conference.

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

[6]  Yunbo Rao,et al.  Hybrid single image dehazing with bright channel and dark channel priors , 2017, 2017 2nd International Conference on Image, Vision and Computing (ICIVC).

[7]  Yi Wang,et al.  Haze editing with natural transmission , 2015, The Visual Computer.

[8]  Jiaya Jia,et al.  Times Faster Weighted Median Filter ( WMF ) , 2014 .

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

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

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

[12]  Moch Arief Soeleman,et al.  Image enhancement segmentation Indonesian's Batik based on fuzzy C-means clustering using median filter , 2017, 2017 International Seminar on Application for Technology of Information and Communication (iSemantic).

[13]  Wang Zhifang,et al.  Color image quality assessment based on noise model of human vision perception and color image quality optimization , 2010 .

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

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

[16]  Bo Wu,et al.  Improved single image dehazing using dark channel prior , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[17]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Zhenyang Wu,et al.  Natural color image enhancement and evaluation algorithm based on human visual system , 2006, Comput. Vis. Image Underst..