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Image Dehazing Using Dark Channel Prior and Fast Guided Filter in Daubechies Lifting Wavelet Transform Domain

Authors: Harpreet Kaur, Sudipta Majumdar


In this paper a method for image dehazing is proposed in lifting wavelet transform domain. Lifting Daubechies (D4) wavelet has been used to obtain the approximate image and detail images.  As the haze is contained in low frequency part, only the approximate image is used for further processing. This region is processed by dehazing algorithm based on dark channel prior (DCP). The dehazed approximate image is then recombined with the detail images using inverse lifting wavelet transform. Implementation of lifting wavelet transform has the advantage of auxiliary memory saving, fast implementation and simplicity. Also, the proposed method deals with near white scene problem, blue horizon issue and localized light sources in a way to enhance image quality and makes the algorithm robust. Simulation results present improvement in terms of visual quality, parameters such as root mean square (RMS) contrast, structural similarity index (SSIM), entropy and execution time.

Keywords: Dark channel prior, image dehazing, lifting wavelet transform.

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[1] Kaiming He, Jian Sun, and Xiaoou Tang, Fellow IEEE, "Single Image Haze Removal Using Dark Channel Prior", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, December 2011
[2] Kaiming He, Jian Sun, “Fast Guided filter”,, arXiv:1505.00996v1(cs.CV), 5 May 2015
[3] Yogesh Kumar, Jimmy Gautam, Ashutosh Gupta, Bhavin V. Kakani and Himansu Chaudhary, “Single Image Dehazing Using Improved Dark Channel Prior”, IEEE conference on Signal Processing and Integrated Networks (SPIN), February 2015
[4] Minmin Yang1, Zhengguo Li, Jianchang Liu, “Super-pixel Based Single Image Haze Removal”, Control and Decision Conference (CCDC), 2016
[5] Bo-Hao Chen and Shih-Chia Huang, “Edge Collapse-Based Dehazing Algorithm for Visibility Restoration in Real Scenes”, IEEE Journal of Display Technology, Volume: 12, Issue: 9, Sept. 2016
[6] Cheng-Hsiung Hsieh, Yu-Sheng Lin and Chih-Hui Chang, “Haze Removal Without Transmission Map Refinement Based On Dual Dark Channels”, IEEE International Conference on Machine Learning and Cybernetics, vol. 2, July 2014
[7] Hung-Yu Yang, Pei-Yin Chen, Chien-Chuan Huang, Ya-Zhu Zhuang, Yeu-Horng Shiau, “Low Complexity Underwater Image Enhancement Based on Dark Channel Prior”, IEEE Conference on Innovations in Bio-inspired Computing and Applications (IBICA), December 2011
[8] Yanjing Yang, Zhizhong Fu, Xinyu Li, Chang Shu and Xiaofeng Li, “A Novel Single Image Dehazing Method” , IEEE International Conference on Computational Problem-solving (ICCP), October 2013
[9] K. He, J. Sun, and X. Tang., “Guided image filtering”, Proceedings of European Conference on Computer Vision (ECCV)
[10] Yingchao Song, Haibo Luo, Bing Hui and Zheng Chang, “An Improved Image Dehazing and Enhancing Method Using Dark Channel Prior”, IEEE Chinese Control and Decision Conference (CCDC), May 2015
[11] Guoling Bi, Jianyue Ren, Tianjiao Fu,Ting Nie, Changzheng Chen, Nan Zhang, “Image Dehazing Based on Accurate Estimation of Transmission in the Atmospheric Scattering Model”, IEEE Photonics Society, Vol 9, August 2017
[12] Chia-Hung Yeh, Li-Wei Kang, Cheng-Yang Lin and Chih-Yang Lin, “Efficient Image/Video Dehazing through Haze Density Analysis Based on Pixel-based Dark Channel Prior”, IEEE Conference on Information Security and Intelligence Control (ISIC), August 2012
[13] Jiajie Liu, Jieying Zheng, Ziguan Cui, Guijin Tang and Feng Liu, “An Improved Image Dehazing Algorithm Based on Dark Channel Prior”, IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA), September 2014
[14] Yi-Jui Cheng, Bo-Hao Chen, Shih-Chia Huang, Sy-Yen Kuo, Andrey Kopylov, Oleg Seredin, Leonid Mestetskiy, Boris Vishnyakov, Yury Vizilter, Oleg Vygolov, Chia-Ruei Lian and Chi-Ting Wu, “Visibility Enhancement of Single Hazy Images Using Hybrid Dark Channel Prior”, IEEE International Conference on Systems, Man, and Cybernetics, October 2013
[15] C. Chengtao, Z. Qiuyu and L. Yanhua, "Improved Dark Channel Prior Dehazing Approach Using Adaptive Factor", IEEE International Conference on Mechatronics and Automation (ICMA), August 2015
[16] Ting Han and Yi Wan, “A Fast Dark Channel Prior-based Depth Map Approximation Method for Dehazing Single Images”, IEEE Third International Conference on Information Science and Technology, March 2013
[17] Xipan Lu, Guoyun Lv and Tao Lei, “Fast Single Image Dehazing Algorithm”, IEEE Conference on Audio, Language and Image Processing (ICALIP), July 2014
[18] Feng Liu and Canmei Yang, “A Fast Method for Single Image Dehazing Using Dark Channel Prior”, IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), August 2014
[19] Nicholas Carlevaris-Bianco, Anush Mohan and Ryan M. Eustice, "Initial Results in Underwater Single Image Dehazing", IEEE Oceans Mts, September 2010
[20] A. Levin, D. Lischinski, and Y. Weiss, “A Closed Form Solution to Natural Image Matting,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 61-68, 2006.
[21] W. Sweldens, "The Lifting Scheme: A Construction of second generation wavelets," SIAMJ. Math. Anal 1997
[22] W. Sweldens, "The Lifting Scheme: A Custom Design construction of Biorthogonal," Wavelets Appl. Comput. Harmon. Anal. 3(2); 1996
[23] I. Daubechies and W. Sweldens "Factoring wavelet transform into lifting steps" Technical report, Bell Laboratories, Lucent Technologies, 1996
[24] W. Sweldens “Wavelets and the lifting scheme: A 5 minute tour,” ZAMM - Journal of Applied Mathematics and Mechanics, vol. 76 (Suppl. 2), pp. 41-44, 1996.