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Li, \u201cRemote sensing image dehazing using heterogeneous atmospheric light prior,\u201d IEEE Access, vol.11, pp.18805-18820, 2023. 10.1109\/access.2023.3247967","DOI":"10.1109\/ACCESS.2023.3247967"},{"key":"5","unstructured":"[5] E. Hadjidemetriou, Use of histograms for recognition, Ph.D. thesis, Columbia University, 2002."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] E.H. Land and J.J. McCann, \u201cLightness and retinex theory,\u201d Josa, vol.61, no.1, pp.1-11, 1971. 10.1364\/josa.61.000001","DOI":"10.1364\/JOSA.61.000001"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] R. Fries and J. Modestino, \u201cImage enhancement by stochastic homomorphic filtering,\u201d IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.27, no.6, pp.625-637, 1979. 10.1109\/tassp.1979.1163324","DOI":"10.1109\/TASSP.1979.1163324"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] K. He, J. Sun, and X. Tang, \u201cSingle image haze removal using dark channel prior,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.33, no.12, pp.2341-2353, 2011. 10.1109\/tpami.2010.168","DOI":"10.1109\/TPAMI.2010.168"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] C. Li, C. Yuan, H. Pan, Y. Yang, Z. Wang, H. Zhou, and H. Xiong, \u201cSingle-image dehazing based on improved bright channel prior and dark channel prior,\u201d Electronics, vol.12, no.2, p.299, 2023. 10.3390\/electronics12020299","DOI":"10.3390\/electronics12020299"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] C. Li, H. Yu, S. Zhou, Z. Liu, Y. Guo, X. Yin, and W. Zhang, \u201cEfficient dehazing method for outdoor and remote sensing images,\u201d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.16, pp.4516-4528, 2023. 10.1109\/jstars.2023.3274779","DOI":"10.1109\/JSTARS.2023.3274779"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] 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":"12","doi-asserted-by":"crossref","unstructured":"[12] W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang, \u201cSingle image dehazing via multi-scale convolutional neural networks,\u201d Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Oct. 11-14, 2016, Proc. Part II 14, pp.154-169, Springer, 2016. 10.1007\/978-3-319-46475-6_10","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, \u201cAod-net: All-in-one dehazing network,\u201d Proc. IEEE International Conference on Computer Vision, pp.4770-4778, 2017.","DOI":"10.1109\/ICCV.2017.511"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] H. Zhang and V.M. 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Wu, Y. Qu, S. Lin, J. Zhou, R. Qiao, Z. Zhang, Y. Xie, and L. Ma, \u201cContrastive learning for compact single image dehazing,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.10551-10560, 2021.","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] W. Ge, Y. Lin, Z. Wang, G. Wang, and S. Tan, \u201cAn improved u-net architecture for image dehazing,\u201d IEICE Trans. Inf. & Syst., vol.E104-D, no.12, pp.2218-2225, 2021. 10.1587\/transinf.2021edp7043","DOI":"10.1587\/transinf.2021EDP7043"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] X. Chen, Y. Li, L. Dai, and C. Kong, \u201cHybrid high-resolution learning for single remote sensing satellite image dehazing,\u201d IEEE Geosci. Remote Sens. Lett., vol.19, pp.1-5, 2021. 10.1109\/lgrs.2021.3072917","DOI":"10.1109\/LGRS.2021.3072917"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] Z. He, C. Gong, Y. Hu, F. Zheng, and L. 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Perlin, \u201cImproving noise,\u201d Proc. 29th annual conference on Computer graphics and interactive techniques, pp.681-682, 2002. 10.1145\/566570.566636","DOI":"10.1145\/566570.566636"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] M.M. Petrou and C. Petrou, Image Processing: The fundamentals, John Wiley & Sons, 2010.","DOI":"10.1002\/9781119994398"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, \u201cImage quality assessment: from error visibility to structural similarity,\u201d IEEE Trans. Image Process., vol.13, no.4, pp.600-612, 2004. 10.1109\/tip.2003.819861","DOI":"10.1109\/TIP.2003.819861"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] R. Zhang, P. Isola, A.A. Efros, E. Shechtman, and O. Wang, \u201cThe unreasonable effectiveness of deep features as a perceptual metric,\u201d Proc. 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