[1] Wang Y, Cao J L. Task feature decoupling model for autonomous driving visual joint perception[J]. Laser & Optoelectronics Progress, 61, 2215007(2024).

[2] Gu H R, Li Z Q, Hou W Z et al. Information content analysis on passive remote sensing imaging retrieval of aerosol layer height based on spaceborne polarization crossfire[J]. Acta Optica Sinica, 43, 0601003(2023).

[3] Zhou S Y, Luo Y H, Zeng Y Q et al. Application of single domain antibody in biomedical imaging(invited)[J]. Chinese Journal of Lasers, 51, 1507102(2024).

[4] Narasimhan S G, Nayar S K. Contrast restoration of weather degraded images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 713-724(2003).

[5] 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).

[6] 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, 25, 5187-5198(2016).

[7] Zhang H, Patel V M. Densely connected pyramid dehazing network[C], 3194-3203(2018).

[8] Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 24, 3522-3533(2015).

[9] Wu X P, Gao Q Q, Huang S W et al. Adaptive Retinex image defogging algorithm based on depth-of-field information[J]. Laser & Optoelectronics Progress, 60, 1210013(2023).

[10] Li B Y, Peng X L, Wang Z Y et al. AOD-net: all-in-one dehazing network[C], 4780-4788(2017).

[11] Li P Y, Tian J D, Tang Y D et al. Deep retinex network for single image dehazing[J]. IEEE Transactions on Image Processing, 30, 1100-1115(2021).

[12] Qin X, Wang Z L, Bai Y C et al. FFA-net: feature fusion attention network for single image dehazing[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11908-11915(2020).

[13] Wu H Y, Qu Y Y, Lin S H et al. Contrastive learning for compact single image dehazing[C], 10546-10555(2021).

[14] Dong H, Pan J S, Xiang L et al. Multi-scale boosted dehazing network with dense feature fusion[C], 2154-2164(2020).

[15] Cao R H, Zhang P C, Wang L et al. Single image defogging algorithm based on attention mechanism[J]. Laser & Optoelectronics Progress, 60, 0210011(2023).

[16] Cui Y N, Tao Y, Jing L X et al. Strip attention for image restoration[C], 645-653(2023).

[17] Chen Z X, He Z W, Lu Z M. DEA-net: single image dehazing based on detail-enhanced convolution and content-guided attention[J]. IEEE Transactions on Image Processing, 33, 1002-1015(2024).

[18] Cui Y N, Ren W Q, Yang S N et al. IRNeXt: rethinking convolutional network design for image restoration[EB/OL]. https://openreview.net/forum?id=MZkbgahv4a

[19] Cui Y N, Ren W Q, Knoll A. Omni-kernel network for image restoration[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 1426-1434(2024).

[20] Cui Y N, Ren W Q, Cao X C et al. Focal network for image restoration[C], 12955-12965(2023).

[21] Cui Y N, Tao Y, Bing Z S et al. Selective frequency network for image restoration[EB/OL]. https://openreview.net/forum?id=tyZ1ChGZIKO

[22] Song Y D, He Z Q, Qian H et al. Vision transformers for single image dehazing[J]. IEEE Transactions on Image Processing, 32, 1927-1941(2023).

[23] Guo C L, Yan Q X, Anwar S et al. Image dehazing transformer with transmission-aware 3D position embedding[C], 5802-5810(2022).

[24] Zhou M, Huang J, Guo C L et al. Fourmer: an efficient global modeling paradigm for image restoration[EB/OL]. https://openreview.net/pdf?id=XMer44w2u9

[25] Prewitt J M S. Object enhancement and extraction[J]. Picture Processing and Psychopictorics, 10, 15-19(1970).

[26] Rosenfeld A. The max Roberts operator is a Hueckel-type edge detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3, 101-103(1981).

[27] Gao W S, Zhang X G, Yang L et al. An improved Sobel edge detection[C], 67-71(2010).

[28] Ding L, Goshtasby A. On the Canny edge detector[J]. Pattern Recognition, 34, 721-725(2001).

[29] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[30] Howard A G, Zhu M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. https://arxiv.org/abs/1704.04861v1

[31] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[32] Guo M H, Lu C Z, Liu Z N et al. Visual attention network[J]. Computational Visual Media, 9, 733-752(2023).

[33] Li J C, Hassani A, Walton S et al. ConvMLP: hierarchical convolutional MLPs for vision[C], 6307-6316(2023).

[34] Li B Y, Ren W Q, Fu D P et al. Benchmarking single image dehazing and beyond[J]. IEEE Transactions on Image Processing, 28, 492-505(2019).

[35] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[36] Ren W Q, Ma L, Zhang J W et al. Gated fusion network for single image dehazing[C], 3253-3261(2018).

[37] Chen D D, He M M, Fan Q N et al. Gated context aggregation network for image dehazing and deraining[C], 1375-1383(2019).

[38] Liu X H, Ma Y R, Shi Z H et al. GridDehazeNet: attention-based multi-scale network for image dehazing[C], 7313-7322(2019).

[39] Dong J X, Pan J S. Physics-based feature dehazing networks[M]. Computer vision-ECCV 2020, 12375, 188-204(2020).