[1] KIM J H, JANG W D et al. Optimized contrast enhancement for real-time image and video dehazing[J]. Journal of Visual Communication and Image Representation, 24, 410-425(2013).

[2] [2] 2刘海波， 杨杰， 吴正平， 等. 基于暗通道先验和Retinex理论的快速单幅图像去雾方法［J］. 自动化学报， 2015， 41（7）： 1264-1273. doi: 10.16383/j.aas.2015.c140748LIUH B， YANGJ， WUZ P， et al. A fast single image dehazing method based on dark channel prior and retinex theory［J］. Acta Automatica Sinica， 2015， 41（7）： 1264-1273.（in Chinese）. doi: 10.16383/j.aas.2015.c140748

[3] [3] 3邓莉. 针对明亮区域的自适应全局暗原色先验去雾［J］. 光学 精密工程， 2016， 24（4）： 892-901. doi: 10.3788/ope.20162404.0892DENGL. Adaptive image dehazing for bright areas based on global dark channel prior［J］. Opt. Precision Eng.， 2016， 24（4）： 892-901.（in Chinese）. doi: 10.3788/ope.20162404.0892

[4] [4] 4张峥， 李奇， 徐之海， 等. 结合颜色线和暗通道的遥感图像去雾［J］. 光学 精密工程， 2019， 27（1）： 181-190. doi: 10.3788/ope.20192701.0181ZHANGZ， LIQ， XUZ H， et al. Color-line and dark channel based dehazing for remote sensing images［J］. Opt. Precision Eng.， 2019， 27（1）： 181-190.（in Chinese）. doi: 10.3788/ope.20192701.0181

[5] [5] 5王一斌， 尹诗白， 吕卓纹. 自适应背景光估计与非局部先验的水下图像复原［J］. 光学 精密工程， 2019， 27（2）： 499-510. doi: 10.3788/OPE.20192702.0499WANGY B， YINS B， LVZH W. Underwater image restoration with adaptive background light estimation and non-local prior［J］. Opt. Precision Eng.， 2019， 27（2）： 499-510.（in Chinese）. doi: 10.3788/OPE.20192702.0499

[6] STARK J A. Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Transactions on Image Processing, 9, 889-896(2000).

[7] LAND E H, MCCANN J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 61, 1-11(1971).

[8] LAND E H. The retinex theory of color vision[J]. Scientific American, 237, 108-128(1977).

[9] NARASIMHAN S G, NAYAR S K. Shedding light on the weather[C], I(2003).

[10] HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 2341-2353(2011).

[11] LEVIN A, LISCHINSKI D, WEISS Y. A closed-form solution to natural image matting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 228-242(2008).

[12] HE K M, SUN J, TANG X. Guided image filtering[C](2010).

[13] ZENG L, DAI Y Z. Single image dehazing based on combining dark channel prior and scene radiance constraint[J]. Chinese Journal of Electronics, 25, 1114-1120(2016).

[14] LU X P, LV G Y, LEI T. Single image dehazing based on multiple scattering model[C], 239-244(2014).

[15] [15] 15陈书贞， 任占广， 练秋生. 基于改进暗通道和导向滤波的单幅图像去雾算法［J］. 自动化学报， 2016， 42（3）： 455-465. doi: 10.16383/j.aas.2016.c150212CHENS Z， RENZ G， LIANQ S. Single image dehazing algorithm based on improved dark channel prior and guided filter［J］. Acta Automatica Sinica， 2016， 42（3）： 455-465.（in Chinese）. doi: 10.16383/j.aas.2016.c150212

[16] ZHU Q S, SHAO L. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing： a Publication of the IEEE Signal Processing Society, 24, 3522-3533(2015).

[17] BERMAN D, TREIBITZ T, AVIDAN S. Non-local image dehazing[C], 1674-1682(2016).

[18] MENG G F, WANG Y, DUAN J Y et al. Efficient image dehazing with boundary constraint and contextual regularization[C], 617-624(2013).

[19] ANCUTI C O, ANCUTI C, DE VLEESCHOUWER C et al. Color channel transfer for image dehazing[J]. IEEE Signal Processing Letters, 26, 1413-1417(2019).

[20] CAI B L, XU X M, JIA K 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, 25, 5187-5198(2016).

[21] LI B Y, PENG X L, WANG Z Y et al. AOD-net： all-in-one dehazing network[C], 4780-4788(2017).

[22] REN W Q, LIU S, ZHANG H et al. Single image dehazing via multi-scale convolutional neural networks[C], 2016, 154-169(2016).

[23] QIAN W, ZHOU C, ZHANG D Y. CIASM-net： a novel convolutional neural network for dehazing image[C], 329-333(2020).

[24] ZHANG J, TAO D C. FAMED-net： a fast and accurate multi-scale end-to-end dehazing network[J]. IEEE Transactions on Image Processing, 29, 72-84(2020).

[25] LI C Y, GUO C L, GUO J C et al. PDR-net： perception-inspired single image dehazing network with refinement[J]. IEEE Transactions on Multimedia, 22, 704-716(2020).

[26] [26] 26陈清江， 张雪. 混合残差学习与导向滤波算法在图像去雾中的应用［J］. 光学 精密工程， 2019， 27（12）： 2702-2712. doi: 10.3788/ope.20192712.2702CHENQ J， ZHANGX. Application of hybrid residual learning and guided filtering algorithm in image defogging［J］. Opt. Precision Eng.， 2019， 27（12）： 2702-2712.（in Chinese）. doi: 10.3788/ope.20192712.2702

[27] NAYAR S K, NARASIMHAN S G. Vision in bad weather[C], 820-827(1999).

[28] KIM J H, JANG W D et al. Optimized contrast enhancement for real-time image and video dehazing[J]. Journal of Visual Communication and Image Representation, 24, 410-425(2013).

[29] [29] 29韩昊男， 钱锋， 吕建威， 等. 改进暗通道先验的航空图像去雾［J］. 光学 精密工程， 2020， 28（6）： 1387-1394. doi: 10.3788/ope.20202806.1387HANH N， QIANF， LÜJ W， et al. Aerial image dehazing using improved dark channel prior［J］. Opt. Precision Eng.， 2020， 28（6）： 1387-1394.（in Chinese）. doi: 10.3788/ope.20202806.1387

[30] [30] 30鞠铭烨， 张登银， 纪应天. 基于雾气浓度估计的图像去雾算法［J］. 自动化学报， 2016， 42（9）： 1367-1379.JUM Y， ZHANGD Y， JIY T. Image haze removal algorithm based on haze thickness estimation［J］. Acta Automatica Sinica， 2016， 42（9）： 1367-1379.（in Chinese）

[31] ANCUTI C, ANCUTI C O, DE VLEESCHOUWER C. D-HAZY： a dataset to evaluate quantitatively dehazing algorithms[C]. AZ, 2226-2230(2016).

[32] MARQUARDT D W. An algorithm for least-squares estimation of nonlinear parameters[J]. Journal of the Society for Industrial and Applied Mathematics, 11, 431-441(1963).

[33] ZHANG L B, WANG S, WANG X H. Single image dehazing based on bright channel prior model and saliency analysis strategy[J]. IET Image Processing, 15, 1023-1031(2021).

[34] HAUTIÈRE N, TAREL J P, AUBERT D et al. Blind contrast enhancement assessment by gradient ratioing at visible edges[J]. Image Analysis & Stereology, 27, 87(2011).

[35] JOBSON D J, RAHMAN Z U, WOODELL G A. Statistics of visual representation[C], 4736, 25-35(4736).