[1]WANG Keping,YANG Yi,FEI Shumin.Review of hazy image sharpening methods[J].CAAI Transactions on Intelligent Systems,2023,18(2):217-230.[doi:10.11992/tis.202110029]
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Review of hazy image sharpening methods

References:
[1] SHEKAR A K, GOU Liang, REN Liu, et al. Label-free robustness estimation of object detection CNNs for autonomous driving applications[J]. International journal of computer vision, 2021, 129(4): 1185–1201.
[2] XIAO H, MOSHREFI A, TURNER A, et al. Methods and systems for automobile security monitoring[P]. US, 20120162423A1, 2012.
[3] LI Yanshan, XIA Rongjie, LIU Xing. Learning shape and motion representations for view invariant skeleton-based action recognition[J]. Pattern recognition, 2020, 103: 107293.
[4] ZHAO Zhongqiu, ZHENG Peng, XU Shoutao, et al. Object detection with deep learning: a review[J]. IEEE transactions on neural networks and learning systems, 2019, 30(11): 3212–3232.
[5] WANG Guangting, LUO Chong, XIONG Zhiwei, et al. SPM-tracker: series-parallel matching for real-time visual object tracking[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3638?3647.
[6] LAND E H. The retinex[J]. American scientist, 1964, 52(2): 247–264.
[7] NAYAR S K, NARASIMHAN S G. Vision in bad weather[C]//Proceedings of the seventh IEEE International Conference on Computer Vision. Kerkyra: IEEE, 1999: 820-827.
[8] NARASIMHAN S G, NAYAR S K. Vision and the atmosphere[J]. International journal of computer vision, 2002, 48(3): 233–254.
[9] NARASIMHAN S G, NAYAR S K. Contrast restoration of weather degraded images[J]. IEEE transactions on pattern analysis and machine intelligence, 2003, 25(6): 713–724.
[10] 杨艳飞. 一般形式正则化的大规模离散线性不适定问题算法的研究[D]. 北京: 清华大学, 2018.
YANG Yanfei. Algorithms for large scale discrete linear ill-posed problems with general-form regularization[D]. Beijing: Tsinghua University, 2018.
[11] XU Zhiyuan, LIU Xiaoming, JI Na. Fog removal from color images using contrast limited adaptive histogram equalization[C]//2009 2nd International Congress on Image and Signal Processing. Tianjin: IEEE, 2009: 1?5.
[12] 牛宏侠, 王春智. 基于HSI空间的沙尘图像增强算法[J]. 北京交通大学学报, 2022, 46(5): 1–8
NIU Hongxia, WANG Chunzhi. Sand-dust image enhancement algorithm based on HSI space[J]. Journal of Beijing Jiaotong University, 2022, 46(5): 1–8
[13] 程新. 基于同态滤波的图像增强算法研究[D]. 西安: 西安邮电大学, 2016.
[14] LI Boyi, REN Wenqi, FU Dengpan, et al. Benchmarking single-image dehazing and beyond[J]. IEEE transactions on image processing, 2019, 28(1): 492–505.
[15] ZHANG Yanfu, DING Li, SHARMA G. HazeRD: an outdoor scene dataset and benchmark for single image dehazing[C]//2017 IEEE International Conference on Image Processing. Beijing: IEEE, 2017: 3205?3209.
[16] SILBERMAN N, HOIEM D, KOHLI P, et al. Indoor Segmentation and Support Inference from RGBD Images[C]//European Conference on Computer Vision. Berlin: Springer, 2012: 746?760.
[17] ANCUTI C, ANCUTI C O, DE VLEESCHOUWER C. D-HAZY: a dataset to evaluate quantitatively dehazing algorithms[C]//2016 IEEE International Conference on Image Processing. Phoenix: IEEE, 2016: 2226-2230.
[18] CAI Bolun, XU Xiangmin, JIA Kui, et al. DehazeNet: an end-to-end system for single image haze removal[J]. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 2016, 25(11): 5187–5198.
[19] ANCUTI C, ANCUTI C O, TIMOFTE R, et al. I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images[M]//Advanced Concepts for Intelligent Vision Systems. Cham: Springer International Publishing, 2018: 620?631.
[20] ANCUTI C O, ANCUTI C, TIMOFTE R, et al. O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 867?8678.
[21] ANCUTI C O, ANCUTI C, TIMOFTE R. NH-HAZE: an image dehazing benchmark with non-homogeneous hazy and haze-free images[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 1798?1805.
[22] PETRO A B, SBERT C, MOREL J M. Multiscale retinex[J]. Image processing on line, 2014, 4: 71–88.
[23] XIE F, TANG M, ZHANG R. Review of image enhancement algorithms based on Retinex[J]. Journal of data acquisition and processing, 2019, 34(1): 1–11.
[24] LAND E H, MCCANN J J. Lightness and retinex theory[J]. Journal of the optical society of America, 1971, 61(1): 1.
[25] 顾振飞, 张登银. 基于变分Retinex模型的雾天图像增强方法[J]. 中国矿业大学学报, 2018, 47(6): 1386–1394
GU Zhenfei, ZHANG Dengyin. A hazy image enhancement method based on the variational Retinex model[J]. Journal of China University of Mining & Technology, 2018, 47(6): 1386–1394
[26] WANG Wei, HE Chuanjiang. A variational model with barrier functionals for retinex[J]. SIAM journal on imaging sciences, 2015, 8(3): 1955–1980.
[27] SWINEHART D F. The beer-lambert law[J]. Journal of chemical education, 1962, 39(7): 333.
[28] HE Kaiming, SUN Jian, TANG Xiaoou. Single image haze removal using dark channel prior[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(12): 2341–2353.
[29] 黄富瑜, 李刚, 邹昌帆, 等. 暗原色先验自适应图像去雾方法[J]. 光电子·激光, 2019, 30(12): 1323–1330
HUANG Fuyu, LI Gang, ZOU Changfan, et al. Adaptive image dehazing based on dark channel prior[J]. Journal of optoelectronics·laser, 2019, 30(12): 1323–1330
[30] 黄鹤, 李昕芮, 宋京, 等. 多尺度窗口的自适应透射率修复交通图像去雾方法[J]. 中国光学, 2019, 12(6): 1311–1320
HUANG He, LI Xinrui, SONG Jing, et al. A traffic image dehaze method based on adaptive transmittance estimation with multi-scale window[J]. Chinese optics, 2019, 12(6): 1311–1320
[31] 杨燕, 王志伟. 基于补偿透射率和自适应雾浓度系数的图像复原算法[J]. 通信学报, 2020, 41(1): 66–75
YANG Yan, WANG Zhiwei. Image restoration algorithm based on compensated transmission and adaptive haze concentration coefficient[J]. Journal on communications, 2020, 41(1): 66–75
[32] ZHU Qingsong, MAI Jiaming, SHAO Ling. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE transactions on image processing, 2015, 24(11): 3522–3533.
[33] 赵小强, 宋昭漾. 多级跳线连接的深度残差网络超分辨率重建[J]. 电子与信息学报, 2019, 41(10): 2501–2508
ZHAO Xiaoqiang, SONG Zhaoyang. Super-resolution reconstruction of deep residual network with multi-level skip connections[J]. Journal of electronics & information technology, 2019, 41(10): 2501–2508
[34] KAWULOK M, BENECKI P, PIECHACZEK S, et al. Deep learning for multiple-image super-resolution[J]. IEEE geoscience and remote sensing letters, 2020, 17(6): 1062–1066.
[35] MAEDA S. Unpaired image super-resolution using pseudo-supervision[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 288?297.
[36] LIU Hongyu, JIANG Bin, XIAO Yi, et al. Coherent semantic attention for image inpainting[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 4169?4178.
[37] REN Wenqi, LIU Si, ZHANG Hua, et al. Single image dehazing via multi-scale convolutional neural networks[M]//Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 154?169.
[38] LI Boyi, PENG Xiulian, WANG Zhangyang, et al. AOD-net: all-in-one dehazing network[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4780?4788.
[39] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137–1149.
[40] AMODIO M, ASSOUEL R, SCHMIDT V, et al. Image-to-image Mapping with Many Domains by Sparse Attribute Transfer[EB/OL].(2020?01?23)[2021?09?12].https://arxiv.org/abs/2006.13291.
[41] 张惊雷, 厚雅伟. 基于改进循环生成式对抗网络的图像风格迁移[J]. 电子与信息学报, 2020, 42(5): 1216–1222
ZHANG Jinglei, HOU Yawei. Image-to-image translation based on improved cycle-consistent generative adversarial network[J]. Journal of electronics & information technology, 2020, 42(5): 1216–1222
[42] PATHAK D, KR?HENBüHL P, DONAHUE J, et al. Context encoders: feature learning by inpainting[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2536?2544.
[43] ZHAO Lei, MO Qihang, LIN Sihuan, et al. UCTGAN: diverse image inpainting based on unsupervised cross-space translation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 5740?5749.
[44] LEDIG C, THEIS L, HUSZáR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[EB/OL].(2016?09?15)[2021?09?12].https://arxiv.org/abs/1609.04802v1.
[45] HYUN S, HEO J P. VarSR: variational super-resolution network for very low resolution images[C]//European Conference on Computer Vision. Cham: Springer, 2020: 431?447.
[46] ZHU H, PENG X, CHANDRASEKHAR V, et al. DehazeGAN: when image dehazing meets differential programming[C]// Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18. Stockholm: International Joint Conferences on Artificial Intelligence, 2018:1234?1240.
[47] ZHANG He, PATEL V M. Densely connected pyramid dehazing network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3194?3203.
[48] DAS S D, DUTTA S. Fast deep multi-patch hierarchical network for nonhomogeneous image dehazing[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 1994?2001.
[49] ZHANG Hongguang, DAI Yuchao, LI Hongdong, et al. Deep stacked hierarchical multi-patch network for image deblurring[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 5971?5979.
[50] WANG Keping, YANG Yi, LI Bingfeng, et al. Uneven image dehazing by heterogeneous twin network[J]. IEEE access, 2020, 8: 118485–118496.
[51] LI Runde, PAN Jinshan, LI Zechao, et al. Single image dehazing via conditional generative adversarial network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8202?8211.
[52] ENGIN D, GENC A, EKENEL H K. Cycle-dehaze: enhanced CycleGAN for single image dehazing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 938?9388.
[53] ZHU Junyan, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2242?2251.
[54] BHARATH RAJ N, VENKETESWARAN N. Single image haze removal using a generative adversarial network[C]//2020 International Conference on Wireless Communications Signal Processing and Networking. Chennai: IEEE, 2020: 37?42.
[55] JéGOU S, DROZDZAL M, VAZQUEZ D, et al. The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1175?1183.
[56] XU K, BA J, KIROS R, et. al. Show, attend and tell: Neural image caption generation with visual attention[EB/OL].(2015?02?10)[2021?09?12].https://arxiv.org/abs/1502.03044.
[57] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all You need[EB/OL].(2017?06?12)[2021?09?12].https://arxiv.org/abs/1706.03762.
[58] WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7794?7803.
[59] ZHANG Yulun, LI Kunpeng, LI Kai, et al. Image super-resolution using very deep residual channel attention networks[C]//European Conference on Computer Vision. Cham: Springer, 2018: 294?310.
[60] QIN Xu, WANG Zhilin, BAI Yuanchao, et al. FFA-net: feature fusion attention network for single image dehazing[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(7): 11908–11915.
[61] ANVARI Z, ATHITSOS V. Dehaze-GLCGAN: unpaired single image de-hazing via adversarial training[EB/OL].(2020?08?15)[2021?09?12].https://arxiv.org/abs/2008.06632.
[62] SCHARSTEIN D, HIRSCHMüLLER H, KITAJIMA Y, et al. High-resolution stereo datasets with subpixel-accurate ground truth[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2014: 31?42.
[63] ANCUTI C O, ANCUTI C, SBERT M, et al. Dense-haze: a benchmark for image dehazing with dense-haze and haze-free images[C]//2019 IEEE International Conference on Image Processing. Taipei: IEEE, 2019: 1014?1018.
[64] BERMAN D, TREIBITZ T, AVIDAN S. Non-local image dehazing[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1674?1682.
[65] GALDRAN A, BRIA A, ALVAREZ-GILA A, et al. On the duality between retinex and image dehazing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8212?8221.
[66] ZHANG He, SINDAGI V, PATEL V M. Multi-scale single image dehazing using perceptual pyramid deep network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 1015?101509.
[67] LIU Xiaohong, MA Yongrui, SHI Zhihao, et al. GridDehazeNet: attention-based multi-scale network for image dehazing[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 7313?7322.
[68] ANCUTI C, ANCUTI C O, DE VLEESCHOUWER C, et al. Day and night-time dehazing by local airlight estimation[J]. IEEE transactions on image processing:a publication of the IEEE Signal Processing Society, 2020, 29: 6264–6275.
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