[1] PENG D X, ZHEN T, LI ZH H. Survey of research methods for low light image enhancement[J]. Computer Engineering and Applications, 59, 14-27(2023).

     彭大鑫, 甄彤, 李智慧. 低光照图像增强研究方法综述[J]. 计算机工程与应用, 59, 14-27(2023).

[2] YUAN H, WANG X X, YAN T H et al. Cross-level feature aggregation image enhancement with dual-path hybrid attention[J]. Opt. Precision Eng., 32, 1538-1551(2024).

     袁姮, 王笑雪, 颜廷昊. 双路混合注意力的跨层次特征聚合图像增强[J]. 光学 精密工程, 32, 1538-1551(2024).

[3] JIANG H, LUO A, FAN H Q et al. Low-light image enhancement with wavelet-based diffusion models[J]. ACM Transactions on Graphics, 42, 1-14(2023).

[4] CHANG ZH W, WANG L ZH, LIANG J et al. Underwater calibration image enhancement based on image block decomposition and fusion[J]. Chinese Optics, 17, 810-822(2024).

     常志文, 王立忠, 梁晋. 基于图像块分解融合的水下标定图像增强[J]. 中国光学（中英文）, 17, 810-822(2024).

[5] AGNES P, AHMAD I, ALBERGO S et al. Sensitivity projections for a dual-phase argon TPC optimized for light dark matter searches through the ionization channel[J]. Physical Review D, 107, 112006(2023).

[6] 田静. 基于特征加权融合的红外图像增强方法[J]. 激光杂志, 45, 155-158(2024).

     TIAN J. Infrared image enhancement method based on feature-weighted fusion[J]. Laser Journal, 45, 155-158(2024).

[7] LI X F, WANG W W, FENG X C et al. Deep parametric Retinex decomposition model for low-light image enhancement[J]. Computer Vision and Image Understanding, 241, 103948(2024).

[8] SINGH K, PARIHAR A S. Illumination estimation for nature preserving low-light image enhancement[J]. The Visual Computer, 40, 121-136(2024).

[9] LORE K G, AKINTAYO A, SARKAR S. LLNet： a deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 61, 650-662(2017).

[10] LV F, LU F, WU J et al. MBLLEN： Low-light image/video enhancement using cnns[C], 220, 4(2018).

[11] WEI C, WANG W J, YANG W H et al. Deep retinex decomposition for low-light enhancement[webpage], 1808-04560. https：//arxiv.org/abs/1808.04560v1

[12] ZHU M F, PAN P B, CHEN W et al. EEMEFN： low-light image enhancement via edge-enhanced multi-exposure fusion network[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 13106-13113(2020).

[13] MA R J, ZHANG B, HU H F. Gaussian pyramid of conditional generative adversarial network for real-world noisy image denoising[J]. Neural Processing Letters, 51, 2669-2684(2020).

[14] XUAN Z, YANG R et al. R2RNet： low-light image enhancement via real-low to real-normal network[J]. Journal of Visual Communication and Image Representation, 90, 103712(2023).

[15] YIN X C, YU Z D, FEI Z T et al. PE-YOLO： pyramid enhancement network forDark object detection[C], 163-174(2023).

[16] YIN J K, WANG Y, GUAN B W et al. Unsupervised underwater image enhancement based on disentangled representations via double-order contrastive loss[J]. IEEE Transactions on Geoscience and Remote Sensing, 62, 3353371(2024).

[17] GAO Z, YANG J, ZHANG L et al. TEGAN： transformer embedded generative adversarial network for underwater image enhancement[J]. Cognitive Computation, 16, 191-214(2024).

[18] ALMAHAIRI A, RAJESHWAR S, SORDONI A et al. Augmented cyclegan： Learning many-to-many mappings from unpaired data[C], 195-204(2018).

[19] JIN Y Y, YANG W H, TAN R T. Unsupervised night image enhancement： when layer decomposition meets light-effects suppression[C], 404-421(2022).

[20] JIANG Y F, GONG X Y, LIU D et al. EnlightenGAN： deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 30, 2340-2349(2021).

[21] LI J J, FENG X M, HUA Z. Low-light image enhancement via progressive-recursive network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 31, 4227-4240(2021).

[22] ZHOU F, SUN X, DONG J Y et al. SurroundNet： Towards effective low-light image enhancement[J]. Pattern Recognition, 141, 109602(2023).

[23] LI J Q, LI J C, FANG F M et al. Luminance-aware pyramid network for low-light image enhancement[J]. IEEE Transactions on Multimedia, 23, 3153-3165(2021).

[24] GUO C L, LI C Y, GUO J C et al. Zero-reference deep curve estimation for low-light image enhancement[C], 13, 1777-1786(2020).

[25] ZHANG Y H, LI Y J, LIN Q. Low-light enhancer for UAV night tracking based on zero-DCE++[J]. Journal of Computer and Communications, 11, 1-11(2023).

[26] MA L, MA T Y, LIU R S et al. Toward fast， flexible， and robust low-light image enhancement[C], 18, 5627-5636(2022).

[27] DAI Y M, GIESEKE F, OEHMCKE S et al. Attentional feature fusion[C], 3, 3559-3568(2021).

[28] 张印辉, 庄宏, 何自芬. 氨气泄漏混洗自注意力轻量化红外检测[J]. 中国光学（中英文）, 16, 607-619(2023).

     ZHANG Y H, ZHUANG H, HE Z F et al. Lightweight infrared detection of ammonia leakage using shuffle and self-attention[J]. Chinese Optics, 16, 607-619(2023).

[29] TIARIMTI-ALAOUI A, JOURHMANE M. Edge-preserving smoothing of Perona-Malik nonlinear diffusion in two-dimensions[J]. International Journal of Nonlinear Analysis and Applications, 2008-6822(2024).

[30] ZHU K, LI L, ZHANG T et al. Survey of vision transformer in low-level computer vision[J]. Computer Engineering and Applications, 60, 39-56(2024).

     朱凯, 李理, 张彤. 视觉Transformer在低级视觉领域的研究综述[J]. 计算机工程与应用, 60, 39-56(2024).

[31] YE Z, CHEN T X, WANG F et al. P-mamba： marrying perona malik diffusion with mamba for efficient pediatric echocardiographic left ventricular segmentation[webpage], 2402-08506. https：//arxiv.org/abs/2402.08506v3

[32] WANG X D, CHEN X A, REN W H et al. Compensation atmospheric scattering model and two-branch network for single image dehazing[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 8, 2880-2896(2024).

[33] FINDER S E, AMOYAL R, TREISTER E et al. Wavelet convolutions for large receptive fields[webpage], 2407-05848. https：//arxiv.org/abs/2407.05848v2

[34] LOH Y P, CHAN C S. Getting to know low-light images with the Exclusively Dark dataset[J]. Computer Vision and Image Understanding, 178, 30-42(2019).

[35] WU Y H, PAN C, WANG G Q et al. Learning semantic-aware knowledge guidance for low-light image enhancement[C], 17, 1662-1671(2023).

[36] YANG S Z, DING M X, WU Y M et al. Implicit neural representation for cooperative low-light image enhancement[C], 1, 12872-12881(2023).