TTV Regularized LRTA Technique for the Estimation of Haze Model Parameters in Video Dehazing
Article No.: 4, Pages 1 - 22
Abstract
Nowadays, intelligent transport systems have a major role in providing a safe and secure traffic society for passengers, pedestrians, and vehicles. However, some bad weather conditions such as haze or fog may affect the visual clarity of video footage captured by the camera. This will cause a malfunction in further video processing algorithms performed by such automated systems. This article proposes an efficient technique for estimating the atmospheric light and the transmission map in the haze model entirely in tensor domain for video dehazing. In this work, the atmospheric light is appraised using the Mie scattering principle of visible light and the temporal coherency among the frames is achieved by means of tensor algebra. Furthermore, the transmission map is computed using Low Rank Tensor Approximation (LRTA) based on Weighted Tensor Nuclear Norm (WTNN) minimization and Tensor Total Variation (TTV) regularization. WTNN minimization is used to smooth the coarse transmission map, and TTV regularization is employed to maintain spatio-temporal continuity by preserving the details of salient structures and edges. The novelty of the proposed model is confined in the efficient formulation of a unified optimization model for the estimation of transmission map and atmospheric light in the tensor domain with fine-tuned regularization terms, which is not reported till now in the direction of video dehazing. Extensive experiments show that the proposed method outperforms state-of-the-art methods in video dehazing.
References
[1]
Ahmad Alajarmeh and A. A. Zaidan. 2018. A real-time framework for video Dehazing using bounded transmission and controlled Gaussian filter. Multim. Tools Applic. 77, 20 (2018), 26315–26350.
[2]
M. Baburaj and Sudhish N. George. 2018. Twist tensor total variation regularized-reweighted nuclear norm-based tensor completion for video missing area recovery. Inf. Sci. 423 (2018), 376–397.
[3]
M. Baburaj and Sudhish N. George. 2019. Tensor-based approach for inpainting of video containing sparse text. Multim. Tools Applic. 78, 2 (2019), 1805–1829.
[4]
P. S. Baiju, P. Deepak Jayan, and Sudhish N. George. 2018. Weighted nuclear norm and TV regularization-based image deraining. In 2018 24th National Conference on Communications (NCC). IEEE, 1–6.
[5]
Sebahattin Bektaş and Yasemin Şişman. 2010. The comparison of L1 and L2-norm minimization methods. Int. J. Phys. Sci. 5, 11 (2010), 1721–1727.
[6]
Gwendoline Blanchet and Lionel Moisan. 2012. An explicit sharpness index related to global phase coherence. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1065–1068.
[7]
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein et al. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1 (2011), 1–122.
[8]
Bolun Cai, Xiangmin Xu, and Dacheng Tao. 2016. Real-time video dehazing based on spatio-temporal MRF. In Pacific Rim Conference on Multimedia. Springer, 315–325.
[9]
Wenfei Cao, Yao Wang, Jian Sun, Deyu Meng, Can Yang, Andrzej Cichocki, and Zongben Xu. 2016. Total variation regularized tensor RPCA for background subtraction from compressive measurements. IEEE Trans. Image Process. 25, 9 (2016), 4075–4090.
[10]
Xiaochun Cao, Liang Yang, and Xiaojie Guo. 2016. Total variation regularized RPCA for irregularly moving object detection under dynamic background. IEEE Trans. Cyber. 46, 4 (2016), 1014–1027.
[11]
J. Douglas Carroll and Jih-Jie Chang. 1970. Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika 35, 3 (1970), 283–319.
[12]
Bo-Hao Chen and Shih-Chia Huang. 2015. An advanced visibility restoration algorithm for single hazy images. ACM Trans. Multim. Comput., Commun. Applic. 11, 4 (2015), 1–21.
[13]
Chen Chen, Minh N. Do, and Jue Wang. 2016. Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In European Conference on Computer Vision. Springer, 576–591.
[14]
P. Comon. 2014. Tensors: A brief introduction. IEEE Sig. Process. Mag. 31, 3 (May 2014), 44–53. DOI:https://doi.org/10.1109/MSP.2014.2298533
[15]
Apurba Das, Shashidhar Pai, Vinayak S. Shenoy, Tanush Vinay, and S. S. Shylaja. 2020. \(D^{2}ehazing\): Real-time dehazing in traffic video analytics by fast dynamic bilateral filtering. In 3rd International Conference on Computer Vision and Image Processing. Springer, 127–137.
[16]
Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle. 2000. A multilinear singular value decomposition. SIAM J. Matrix Anal. Applic. 21, 4 (2000), 1253–1278.
[17]
Tianyang Dong, Guoqing Zhao, Jiamin Wu, Yang Ye, and Ying Shen. 2019. Efficient traffic video dehazing using adaptive dark channel prior and spatial–temporal correlations. Sensors 19, 7 (2019), 1593.
[18]
Theodore L. Economopoulos, Pantelis A. Asvestas, and George K. Matsopoulos. 2010. Contrast enhancement of images using partitioned iterated function systems. Image Vis. Comput. 28, 1 (2010), 45–54.
[19]
Simon Emberton, Lars Chittka, and Andrea Cavallaro. 2018. Underwater image and video dehazing with pure haze region segmentation. Comput. Vis. Image Underst. 168 (2018), 145–156.
[20]
K. B. Gibson, D. T. Vo, and T. Q. Nguyen. 2012. An investigation of dehazing effects on image and video coding. IEEE Trans. Image Process. 21, 2 (Feb. 2012), 662–673. DOI:https://doi.org/10.1109/TIP.2011.2166968
[21]
Nicolas Hautière, Jean-Philippe Tarel, Didier Aubert, and Eric Dumont. 2008. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27, 2 (2008), 87–95.
[22]
K. He, J. Sun, and X. Tang. 2011. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 12 (Dec. 2011), 2341–2353. DOI:https://doi.org/10.1109/TPAMI.2010.168
[23]
Kaiming He, Jian Sun, and Xiaoou Tang. 2012. Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35, 6 (2012), 1397–1409.
[24]
Fei Jiang, Xiao-Yang Liu, Hongtao Lu, and Ruimin Shen. 2018. Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1363–1367.
[25]
Eric Kernfeld, Misha Kilmer, and Shuchin Aeron. 2015. Tensor–tensor products with invertible linear transforms. Linear Algeb. Appl. 485 (2015), 545–570.
[26]
Misha E. Kilmer, Karen Braman, Ning Hao, and Randy C. Hoover. 2013. Third-order tensors as operators on matrices: A theoretical and computational framework with applications in imaging. SIAM J. Matrix Anal. Appl. 34, 1 (2013), 148–172.
[27]
Jin-Hwan Kim, Won-Dong Jang, Jae-Young Sim, and Chang-Su Kim. 2013. Optimized contrast enhancement for real-time image and video dehazing. J. Vis. Commun. Image Repres. 24, 3 (2013), 410–425.
[28]
Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. 2018. End-to-end united video dehazing and detection. In 32nd AAAI Conference on Artificial Intelligence.
[29]
Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. 2018. Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28, 1 (2018), 492–505.
[30]
Mading Li, Jiaying Liu, Xiaoyan Sun, and Zhiwei Xiong. 2019. Image/Video restoration via multiplanar autoregressive model and low-rank optimization. ACM Trans. Multim. Comput., Commun. Applic. 15, 4 (2019), 1–23.
[31]
Z. Li, P. Tan, R. T. Tan, D. Zou, Steven Zhiying Zhou, and L. Cheong. 2015. Simultaneous video defogging and stereo reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 4988–4997. DOI:https://doi.org/10.1109/CVPR.2015.7299133
[32]
Xiaodan Lin and Xiangui Kang. 2018. Robust electric network frequency estimation with rank reduction and linear prediction. ACM Trans. Multim. Comput., Commun. Applic. 14, 4 (2018), 1–13.
[33]
Zhong Luan, Hao Zeng, Yuanyuan Shang, Zhuhong Shao, and Hui Ding. 2018. Fast video dehazing using per-pixel minimum adjustment. Math. Prob. Eng. 2018 (2018), 1–8. DOI:https://doi.org/10.1155/2018/9241629
[34]
Baburaj Madathil and Sudhish N. George. 2018. DCT-based weighted adaptive multi-linear data completion and denoising. Neurocomputing 318 (2018), 120–136.
[35]
Carla D. Martin, Richard Shafer, and Betsy LaRue. 2013. An order-p tensor factorization with applications in imaging. SIAM J. Sci. Comput. 35, 1 (2013), A474–A490.
[36]
Earl J. McCartney. 1976. Optics of the Atmosphere: Scattering by Molecules and Particles. New York, John Wiley and Sons, Inc.
[37]
Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 12 (2012), 4695–4708.
[38]
Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. 2012. Making a “completely blind” image quality analyzer. IEEE Sig. Process. Lett. 20, 3 (2012), 209–212.
[39]
Y. Park and T. Kim. 2018. Fast execution schemes for dark-channel-prior-based outdoor video dehazing. IEEE Access 6 (2018), 10003–10014. DOI:https://doi.org/10.1109/ACCESS.2018.2806378
[40]
Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. 2018. Gated fusion network for single image dehazing. In IEEE Conference on Computer Vision and Pattern Recognition. 3253–3261.
[41]
Wenqi Ren, Jinshan Pan, Hua Zhang, Xiaochun Cao, and Ming-Hsuan Yang. 2020. Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int. J. Comput. Vis. 128, 1 (2020), 240–259.
[42]
Wenqi Ren, Jingang Zhang, Xiangyu Xu, Lin Ma, Xiaochun Cao, Gaofeng Meng, and Wei Liu. 2019. Deep video dehazing with semantic segmentation. IEEE Trans. Image Process. 28, 4 (2019), 1895–1908.
[43]
D. K. Shin, Y. M. Kim, K. T. Park, D. Lee, W. Choi, and Y. S. Moon. 2014. Video dehazing without flicker artifacts using adaptive temporal average. In 18th IEEE International Symposium on Consumer Electronics (ISCE). 1–2. DOI:https://doi.org/10.1109/ISCE.2014.6884454
[44]
Huailiang Tan, Xiaofei He, Zijian Wang, and Gaoming Liu. 2017. Parallel implementation and optimization of high definition video real-time dehazing. Multim. Tools Applic. 76, 22 (2017), 23413–23434.
[45]
Anju Jose Tom and Sudhish N. George. 2018. Tensor total variation regularized moving object detection for surveillance videos. In International Conference on Signal Processing and Communications (SPCOM). IEEE, 327–331.
[46]
Anju Jose Tom and Sudhish N. George. 2020. Simultaneous reconstruction and moving object detection from compressive sampled surveillance videos. IEEE Trans. Image Process. 29 (2020), 7590–7602.
[47]
A. J. Tom and S. N. George. 2021. A three-way optimization technique for noise robust moving object detection using tensor low-rank approximation, l1/2, and TTV regularizations. IEEE Trans. Cyber. 51, 2 (2021), 1004–1014. DOI:https://doi.org/10.1109/TCYB.2019.2921827
[48]
Chia-Chi Tsai, Cheng-Yen Lin, and Jiun-In Guo. 2019. Dark channel prior-based video dehazing algorithm with sky preservation and its embedded system realization for ADAS applications. Optics Expr. 27, 9 (2019), 11877–11901.
[49]
Meihua Wang, Jiaming Mai, Yun Liang, Ruichu Cai, Tom Zhengjia Fu, and Zhenjie Zhang. 2018. A component-driven distributed framework for real-time video dehazing. Multim. Tools Applic. 77, 9 (2018), 11259–11276.
[50]
Chunxia Xiao and Jiajia Gan. 2012. Fast image dehazing using guided joint bilateral filter. Vis. Comput. 28, 6–8 (2012), 713–721.
[51]
Sen Yang, Jie Wang, Wei Fan, Xiatian Zhang, Peter Wonka, and Jieping Ye. 2013. An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems. In 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 641–649.
[52]
Jieping Ye. 2005. Generalized low rank approximations of matrices. Mach. Learn. 61, 1–3 (2005), 167–191.
[53]
Jing Yu, Dong-bin Xu, and Qing-min Liao. 2011. Image defogging: A survey. J. Image Graph. 16, 9 (2011), 1561–1576.
[54]
Jiawan Zhang, Liang Li, Yi Zhang, Guoqiang Yang, Xiaochun Cao, and Jizhou Sun. 2011. Video dehazing with spatial and temporal coherence. Vis. Comput. 27, 6–8 (2011), 749–757.
[55]
Zemin Zhang, Gregory Ely, Shuchin Aeron, Ning Hao, and Misha Kilmer. 2014. Novel methods for multilinear data completion and de-noising based on tensor-SVD. In IEEE Conference on Computer Vision and Pattern Recognition. 3842–3849.
[56]
Xintao Zhao, Wenrui Ding, Chunhui Liu, and Hongguang Li. 2018. Haze removal for unmanned aerial vehicle aerial video based on spatial-temporal coherence optimisation. IET Image Process. 12, 1 (2018), 88–97.
[57]
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13, 4 (Apr. 2004), 600–612. DOI:https://doi.org/10.1109/TIP.2003.819861
[58]
Lei Zhu, Chi-Wing Fu, Yueming Jin, Mingqiang Wei, Jing Qin, and Pheng-Ann Heng. 2016. Non-local sparse and low-rank regularization for structure-preserving image smoothing. In Computer Graphics Forum, Vol. 35. Wiley Online Library, 217–226.
[59]
Qingsong Zhu, Jiaming Mai, and Ling Shao. 2015. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24, 11 (2015), 3522–3533.
Index Terms
- TTV Regularized LRTA Technique for the Estimation of Haze Model Parameters in Video Dehazing
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Publication History
Published: 27 January 2022
Accepted: 01 May 2021
Revised: 01 March 2021
Received: 01 June 2020
Published in TOMM Volume 18, Issue 1
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