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Sun, and X. Tang, \u201cSingle image haze removal using dark channel prior,\u201d 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1956-1963, 2009. 10.1109\/cvpr.2009.5206515"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] Q. Zhu, J. Mai, and L. Shao, \u201cA fast single image haze removal algorithm using color attenuation prior,\u201d IEEE Trans. Image Process., vol.24, no.11, pp.3522-3533, 2015. 10.1109\/tip.2015.2446191","DOI":"10.1109\/TIP.2015.2446191"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] R. Fattal, \u201cSingle image dehazing,\u201d Acm Transactions on Graphics, vol.27, no.3, pp.1-9, 2008. 10.1145\/1360612.1360671","DOI":"10.1145\/1360612.1360671"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Y. Lin, D. Guo, J. Zhang, Z. Chen, and B. Yang, \u201cA unified framework for multilingual speech recognition in air traffic control systems,\u201d IEEE Trans. Neural Netw. Learn. Syst., 2020.","DOI":"10.1109\/TNNLS.2020.3015830"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] Y. Lin, L. Li, H. Jing, B. Ran, and D. Sun, \u201cAutomated traffic incident detection with a smaller dataset based on generative adversarial networks,\u201d Accident; analysis and prevention, 2020.","DOI":"10.1016\/j.aap.2020.105628"},{"key":"10","unstructured":"[10] Y. Lin, L. Deng, Z. Chen, X. Wu, J. Zhang, and B. Yang, \u201cA real-time atc safety monitoring framework using a deep learning approach,\u201d IEEE Trans. Intell. Transp. Syst., pp.1-10, 2019."},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] J.-H. Kim, B.-G. Kim, P.P. Roy, and D.-M. Jeong, \u201cEfficient facial expression recognition algorithm based on hierarchical deep neural network structure,\u201d IEEE Access, vol.7, pp.41273-41285, 2019. 10.1109\/access.2019.2907327","DOI":"10.1109\/ACCESS.2019.2907327"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] M. Chhetri, S. Kumar, P.P. Roy, and B.-G. Kim, \u201cDeep blstm-gru model for monthly rainfall prediction: A case study of simtokha, bhutan,\u201d Remote Sensing, vol.12, no.19, 2020. 10.3390\/rs12193174","DOI":"10.3390\/rs12193174"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] D. Jeong, B.-G. Kim, and S.-Y. Dong, \u201cDeep joint spatiotemporal network (djstn) for efficient facial expression recognition,\u201d Sensors, vol.20, no.7, 2020. 10.3390\/s20071936","DOI":"10.3390\/s20071936"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, \u201cDehazenet: An end-to-end system for single image haze removal,\u201d IEEE Trans. Image Process., vol.25, no.11, pp.5187-5198, 2016. 10.1109\/tip.2016.2598681","DOI":"10.1109\/TIP.2016.2598681"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.H. Yang, Single Image Dehazing via Multi-scale Convolutional Neural Networks, Springer International Publishing, 2016.","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, \u201cAod-net: All-in-one dehazing network,\u201d 2017 IEEE International Conference on Computer Vision (ICCV), 2017. 10.1109\/iccv.2017.511","DOI":"10.1109\/ICCV.2017.511"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] D. Chen, M. He, Q. Fan, J. Liao, L. Zhang, D. Hou, L. Yuan, and G. Hua, \u201cGated context aggregation network for image dehazing and deraining,\u201d 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.1375-1383, IEEE, 2019. 10.1109\/wacv.2019.00151","DOI":"10.1109\/WACV.2019.00151"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] X. Yang, Z. Xu, and J. Luo, \u201cTowards perceptual image dehazing by physics-based disentanglement and adversarial training,\u201d AAAI, 2018.","DOI":"10.1609\/aaai.v32i1.12317"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] J. Johnson, A. Alahi, and L. Fei-Fei, \u201cPerceptual losses for real-time style transfer and super-resolution,\u201d European conference on computer vision, pp.694-711, Springer, 2016. 10.1007\/978-3-319-46475-6_43","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] X. Liu, Y. Ma, Z. Shi, and J. Chen, \u201cGriddehazenet: Attention-based multi-scale network for image dehazing,\u201d ICCV, 2019. 10.1109\/iccv.2019.00741","DOI":"10.1109\/ICCV.2019.00741"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] R. Girshick, \u201cFast R-CNN,\u201d Proc. International Conference on Computer Vision (ICCV), 2015. 10.1109\/iccv.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"22","unstructured":"[22] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d arXiv preprint arXiv:1409.1556, 2014."},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, and L. Fei-Fei, \u201cImagenet large scale visual recognition challenge,\u201d International journal of computer vision, vol.115, no.3, pp.211-252, 2015. 10.1007\/s11263-015-0816-y","DOI":"10.1007\/s11263-015-0816-y"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] B. Li, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang, \u201cBenchmarking single-image dehazing and beyond,\u201d IEEE Trans. Image Process., vol.28, no.1, pp.492-505, 2018. 10.1109\/tip.2018.2867951","DOI":"10.1109\/TIP.2018.2867951"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, \u201cIndoor segmentation and support inference from rgbd images,\u201d European conference on computer vision, pp.746-760, Springer, 2012. 10.1007\/978-3-642-33715-4_54","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"26","unstructured":"[26] D. Scharstein and R. Szeliski, \u201cHigh-accuracy stereo depth maps using structured light,\u201d 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, pp.I-I, 2003. 10.1109\/cvpr.2003.1211354"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] Liu, Fayao, Shen, Chunhua, Lin, Guosheng, Reid, and Ian., \u201cLearning depth from single monocular images using deep convolutional neural fields,\u201d IEEE Trans. Pattern Anal. Mach. Intell., 2016.","DOI":"10.1109\/TPAMI.2015.2505283"},{"key":"28","unstructured":"[28] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, 2014."},{"key":"29","unstructured":"[29] P. Ramachandran, B. 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