Image Haze Removal Using Scene Depth Based Spatially Varying Atmospheric Light in Haar Lifting Wavelet Domain
This paper presents a method for single image dehazing based on dark channel prior (DCP). The property that the intensity of the dark channel gives an approximate thickness of the haze is used to estimate the transmission and atmospheric light. Instead of constant atmospheric light, the proposed method employs scene depth to estimate spatially varying atmospheric light as it truly occurs in nature. Haze imaging model together with the soft matting method has been used in this work to produce high quality haze free image. Experimental results demonstrate that the proposed approach produces better results than the classic DCP approach as color fidelity and contrast of haze free image are improved and no over-saturation in the sky region is observed. Further, lifting Haar wavelet transform is employed to reduce overall execution time by a factor of two to three as compared to the conventional approach.
[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] X. Zhao, W. Ding, C. Liu and H. Li, “Haze Removal For Unmanned Aerial Vehicle Aerial Video Based On Spatial-Temporal Coherence Optimisation.”, IET Image Processing, vol. 12, no. 1, pp. 88-97, 2017.
[3] G. Bi, J. Ren, T. Fu, T. Nie, C. Chen and N. Zhang, “Image Dehazing Based On Accurate Estimation Of Transmission In The Atmospheric Scattering Model”, IEEE Photonics Journal, vol. 9, no. 4, pp.1-18, 2017.
[4] C. Qing, Y. Hu, X. Xu, W. Huang, “Image Haze Removal Using Depth-Based Cluster And Self-Adaptive Parameters”, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1070-1075, 2017.
[5] Nicholas Carlevaris-Bianco, Anush Mohan and Ryan M. Eustice, "Initial Results in Underwater Single Image Dehazing", IEEE Oceans Mts, September 2010
[6] 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 2013Kaiming He, Jian Sun, “Fast Guided filter”, Microsoft.com, arXiv:1505.00996v1(cs.CV), 5 May 2015
[7] W. Sweldens, "The Lifting Scheme: A Construction of second generation wavelets," SIAMJ. Math. Anal 1997
[8] W. Sweldens, "The Lifting Scheme: A Custom Design construction of Biorthogonal," Wavelets Appl. Comput. Harmon. Anal. 3(2); 1996
[9] I. Daubechies and W. Sweldens "Factoring wavelet transform into lifting steps" Technical report, Bell Laboratories, Lucent Technologies, 1996
[10] 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
[11] “Image Dehazing Using Dark Channel Prior and Fast Guided Filter in Daubechies Lifting Wavelet Transform Domain”, World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering Vol:12, No:3, 2018
[12] 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
[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] X. Zhao, W. Ding, C. Liu and H. Li, “Haze Removal For Unmanned Aerial Vehicle Aerial Video Based On Spatial-Temporal Coherence Optimisation.”, IET Image Processing, vol. 12, no. 1, pp. 88-97, 2017.
[3] G. Bi, J. Ren, T. Fu, T. Nie, C. Chen and N. Zhang, “Image Dehazing Based On Accurate Estimation Of Transmission In The Atmospheric Scattering Model”, IEEE Photonics Journal, vol. 9, no. 4, pp.1-18, 2017.
[4] C. Qing, Y. Hu, X. Xu, W. Huang, “Image Haze Removal Using Depth-Based Cluster And Self-Adaptive Parameters”, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1070-1075, 2017.
[5] Nicholas Carlevaris-Bianco, Anush Mohan and Ryan M. Eustice, "Initial Results in Underwater Single Image Dehazing", IEEE Oceans Mts, September 2010
[6] 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 2013Kaiming He, Jian Sun, “Fast Guided filter”, Microsoft.com, arXiv:1505.00996v1(cs.CV), 5 May 2015
[7] W. Sweldens, "The Lifting Scheme: A Construction of second generation wavelets," SIAMJ. Math. Anal 1997
[8] W. Sweldens, "The Lifting Scheme: A Custom Design construction of Biorthogonal," Wavelets Appl. Comput. Harmon. Anal. 3(2); 1996
[9] I. Daubechies and W. Sweldens "Factoring wavelet transform into lifting steps" Technical report, Bell Laboratories, Lucent Technologies, 1996
[10] 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
[11] “Image Dehazing Using Dark Channel Prior and Fast Guided Filter in Daubechies Lifting Wavelet Transform Domain”, World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering Vol:12, No:3, 2018
[12] 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
@article{"International Journal of Information, Control and Computer Sciences:79664", author = "Prabh Preet Singh and Harpreet Kaur", title = "Image Haze Removal Using Scene Depth Based Spatially Varying Atmospheric Light in Haar Lifting Wavelet Domain", abstract = "This paper presents a method for single image dehazing based on dark channel prior (DCP). The property that the intensity of the dark channel gives an approximate thickness of the haze is used to estimate the transmission and atmospheric light. Instead of constant atmospheric light, the proposed method employs scene depth to estimate spatially varying atmospheric light as it truly occurs in nature. Haze imaging model together with the soft matting method has been used in this work to produce high quality haze free image. Experimental results demonstrate that the proposed approach produces better results than the classic DCP approach as color fidelity and contrast of haze free image are improved and no over-saturation in the sky region is observed. Further, lifting Haar wavelet transform is employed to reduce overall execution time by a factor of two to three as compared to the conventional approach.
", keywords = "Depth based atmospheric light, dark channel prior, lifting wavelet.", volume = "14", number = "6", pages = "209-6", }
