{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:56:38Z","timestamp":1775066198213,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Jilin Province","award":["20220101190JC"],"award-info":[{"award-number":["20220101190JC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Low-light image enhancement aims to improve the perceptual quality of images captured under low-light conditions. This paper proposes a novel generative adversarial network to enhance low-light image quality. Firstly, it designs a generator consisting of residual modules with hybrid attention modules and parallel dilated convolution modules. The residual module is designed to prevent gradient explosion during training and to avoid feature information loss. The hybrid attention module is designed to make the network pay more attention to useful features. A parallel dilated convolution module is designed to increase the receptive field and capture multi-scale information. Additionally, a skip connection is utilized to fuse shallow features with deep features to extract more effective features. Secondly, a discriminator is designed to improve the discrimination ability. Finally, an improved loss function is proposed by incorporating pixel loss to effectively recover detailed information. The proposed method demonstrates superior performance in enhancing low-light images compared to seven other methods.<\/jats:p>","DOI":"10.3390\/e25060932","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:26:56Z","timestamp":1686709616000},"page":"932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Unsupervised Low-Light Image Enhancement Based on Generative Adversarial Network"],"prefix":"10.3390","volume":"25","author":[{"given":"Wenshuo","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9499-1911","authenticated-orcid":false,"given":"Liquan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China"}]},{"given":"Tie","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dai, Y., and Liu, W. (2023). GL-YOLO-Lite: A Novel Lightweight Fallen Person Detection Model. Entropy, 25.","DOI":"10.3390\/e25040587"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"21405","DOI":"10.1109\/TITS.2022.3177615","article-title":"SFNet-N: An Improved SFNet Algorithm for Semantic Segmentation of Low-Light Autonomous Driving Road Scenes","volume":"23","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1109\/TMM.2020.3039361","article-title":"DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement","volume":"23","author":"Lim","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/TETCI.2021.3053253","article-title":"Optimized Bezier Curve Based Intensity Mapping Scheme for Low Light Image Enhancement","volume":"6","author":"Veluchamy","year":"2021","journal-title":"IEEE Trans. Emerg. Topics Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, Y.-S., Wang, Y.-C., Kao, M.-H., and Chuang, Y.-Y. (2018, January 18-23). Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00660"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/LSP.2022.3163686","article-title":"Unsupervised Low-Light Image Enhancement by Extracting Structural Similarity and Color Consistency","volume":"29","author":"Shi","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4897","DOI":"10.1109\/TIP.2022.3189805","article-title":"Low-Light Enhancement Using a Plug-and-Play Retinex Model with Shrinkage Mapping for Illumination Estimation","volume":"31","author":"Lin","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5862","DOI":"10.1109\/TIP.2020.2984098","article-title":"LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model","volume":"29","author":"Ren","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep Retinex decomposition for low-light enhancement. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., and Liu, J. (2020, January 13\u201319). From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00313"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_12","first-page":"4225","article-title":"Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation","volume":"44","author":"Li","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4094","DOI":"10.1007\/s10489-020-02016-4","article-title":"Unsupervised medical images denoising via graph attention dual adversarial network","volume":"51","author":"Lv","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1007\/s11554-019-00925-3","article-title":"Deep learning methods in real-time image super-resolution: A survey","volume":"17","author":"Li","year":"2020","journal-title":"J. Real Time Image Proc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1007\/s11554-020-00973-0","article-title":"Optimized highway deep learning network for fast single image super-resolution reconstruction","volume":"17","author":"Ha","year":"2020","journal-title":"J. Real Time Image Proc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ren, Z., Zhang, Y., and Wang, S. (2022). A Hybrid Framework for Lung Cancer Classification. Electronics, 11.","DOI":"10.3390\/electronics11101614"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hua, W., and Xia, Y. (2018, January 13\u201315). Low-Light Image Enhancement Based on Joint Generative Adversarial Network and Image Quality Assessment. Proceedings of the 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Beijing, China.","DOI":"10.1109\/CISP-BMEI.2018.8633150"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, G., Kwon, D., and Kwon, J. (2019, January 22\u201325). Low-Lightgan: Low-Light Enhancement Via Advanced Generative Adversarial Network with Task-Driven Training. Proceedings of the International Conference on Image Processing, Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803328"},{"key":"ref_19","unstructured":"Shi, Y., Wu, X., and Zhu, M. (2019). Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, L., Wan, R., and Su, G. (2021, January 19\u201322). Multi-Scale Feature Guided Low-Light Image Enhancement. Proceedings of the International Conference on Image Processing, Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506785"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/LSP.2022.3167331","article-title":"Rethinking Low-Light Enhancement via Transformer-GAN","volume":"29","author":"Yang","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep Light Enhancement Without Paired Supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1049\/ipr2.12124","article-title":"Generative adversarial network for low-light image enhancement","volume":"15","author":"Li","year":"2021","journal-title":"IET Image Process."},{"key":"ref_24","unstructured":"Qu, Y., Chen, K., Liu, C., and Ou, Y. (June, January 30). UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement. Proceedings of the International Conference on Robotics and Automation, Xi\u2019an, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/LSP.2021.3079848","article-title":"Seeing in the Dark by Component-GAN","volume":"28","author":"Rao","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_26","first-page":"147","article-title":"Alpha-rooting and correlation method of image enhancement","volume":"12100","author":"Grigoryan","year":"2022","journal-title":"Multimodal Image Exploit. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-Light Image Enhancement via Illumination Map Estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Appina, B. (2020, January 19\u201324). A \u2018Complete Blind\u2019 No-Reference Stereoscopic Image Quality Assessment Algorithm. Proceedings of the 2020 International Conference on Signal Processing and Communications (SPCOM), Bangalore, India.","DOI":"10.1109\/SPCOM50965.2020.9179556"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Alqawasmi, K. (2022, January 17\u201320). Estimation of ARMA Model Order Utilizing Structural Similarity Index Algorithm. Proceedings of the 8th International Conference on Control, Decision and Information Technologies, Istanbul, Turkey.","DOI":"10.1109\/CoDIT55151.2022.9804102"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Javaheri, A., Brites, C., Pereira, F., and Ascenso, J. (2020, January 25\u201328). Improving Psnr-Based Quality Metrics Performance for Point Cloud Geometry. Proceedings of the 27th International Conference on Image Processing (ICIP 2020), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9191233"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-Reference Image Quality Assessment in the Spatial Domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TIP.2018.2794218","article-title":"Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images","volume":"27","author":"Cai","year":"2018","journal-title":"IEEE Trans Image Process"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/6\/932\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:18Z","timestamp":1760126058000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/6\/932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,13]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["e25060932"],"URL":"https:\/\/doi.org\/10.3390\/e25060932","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,13]]}}}