{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T17:42:29Z","timestamp":1763142149984,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T00:00:00Z","timestamp":1696636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["22KJB520036","3KJB510033","BK20211357"],"award-info":[{"award-number":["22KJB520036","3KJB510033","BK20211357"]}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["22KJB520036","3KJB510033","BK20211357"],"award-info":[{"award-number":["22KJB520036","3KJB510033","BK20211357"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Underwater autonomous driving devices, such as autonomous underwater vehicles (AUVs), rely on visual sensors, but visual images tend to produce color aberrations and a high turbidity due to the scattering and absorption of underwater light. To address these issues, we propose the Dense Residual Generative Adversarial Network (DRGAN) for underwater image enhancement. Firstly, we adopt a multi-scale feature extraction module to obtain a range of information and increase the receptive field. Secondly, a dense residual block is proposed, to realize the interaction of image features and ensure stable connections in the feature information. Multiple dense residual modules are connected from beginning to end to form a cyclic dense residual network, producing a clear image. Finally, the stability of the network is improved via adjustment to the training with multiple loss functions. Experiments were conducted using the RUIE and Underwater ImageNet datasets. The experimental results show that our proposed DRGAN can remove high turbidity from underwater images and achieve color equalization better than other methods.<\/jats:p>","DOI":"10.3390\/s23198297","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T06:16:48Z","timestamp":1696832208000},"page":"8297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["DRGAN: Dense Residual Generative Adversarial Network for Image Enhancement in an Underwater Autonomous Driving Device"],"prefix":"10.3390","volume":"23","author":[{"given":"Jin","family":"Qian","sequence":"first","affiliation":[{"name":"College of Information Engineering, Taizhou University, Taizhou 225300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Taizhou University, Taizhou 225300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Taizhou University, Taizhou 225300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sen","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoshuang","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215506, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"033012","DOI":"10.1117\/1.JEI.25.3.033012","article-title":"Single underwater image enhancement based on color cast removal and visibility restoration","volume":"25","author":"Li","year":"2016","journal-title":"J. Electron. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, P., Sun, C., Wang, R., and Zhao, X. (2022). Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review. Sensors, 22.","DOI":"10.3390\/s22239316"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Drews, P., Nascimento, E., Moraes, F., Botelho, S., and Campos, M. (2013, January 2\u20138). Transmission estimation in underwater single images. Proceedings of the IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.113"},{"key":"ref_4","first-page":"2341","article-title":"Single Image Haze Removal Using Dark Channel Prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"053033","DOI":"10.1117\/1.JEI.28.5.053033","article-title":"Underwater image restoration through a combination of improved dark channel prior and gray world algorithms","volume":"28","author":"Ma","year":"2019","journal-title":"J. Electron. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1109\/TIP.2017.2759252","article-title":"Color balance and fusion for underwater image enhancement","volume":"27","author":"Ancuti","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.neucom.2020.03.091","article-title":"Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing","volume":"425","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Marques, T.P., and Albu, A.B. (2020, January 14\u201319). L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00277"},{"key":"ref_9","first-page":"387","article-title":"WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images","volume":"3","author":"Li","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fabbri, C., Islam, M.J., and Sattar, J. (2018, January 21\u201325). Enhancing underwater imagery using generative adversarial networks. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460552"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/JOE.2019.2911447","article-title":"Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network","volume":"45","author":"Guo","year":"2019","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1109\/LRA.2020.2974710","article-title":"Fast Underwater Image Enhancement for Improved Visual Perception","volume":"5","author":"Islam","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9350","DOI":"10.1109\/TIE.2019.2893840","article-title":"Towards Real-Time Advancement of Underwater Visual Quality With GAN","volume":"66","author":"Chen","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, S., Wang, K., Liu, H., Chen, J., and Li, Y. (2023, January 17\u201324). Contrastive Semi-Supervised Learning for Underwater Image Restoration via Reliable Bank. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01740"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_16","first-page":"1975","article-title":"Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles","volume":"137","author":"Wei","year":"2023","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, C., and Wand, M. (2016, January 17). Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"0901002","DOI":"10.3788\/AOS201939.0901002","article-title":"Image color correction based on double transmission underwater imaging model","volume":"39","author":"Wang","year":"2019","journal-title":"Acta Opt. Sin."},{"key":"ref_21","unstructured":"Yadav, A., Upadhyay, M., and Singh, G. (2021). Underwater Image Enhancement Using Convolutional Neural Network. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4861","DOI":"10.1109\/TCSVT.2019.2963772","article-title":"Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light","volume":"30","author":"Liu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.imavis.2018.11.001","article-title":"Fast and robust multiple ColorChecker detection using deep convolutional neural networks","volume":"81","author":"Ren","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6062","DOI":"10.1109\/TIP.2015.2491020","article-title":"An Underwater Color Image Quality Evaluation Metric","volume":"24","author":"Yang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/JOE.2015.2469915","article-title":"Human-Visual-System-Inspired Underwater Image Quality Measures","volume":"41","author":"Panetta","year":"2015","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/col.20070","article-title":"The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations","volume":"30","author":"Sharma","year":"2005","journal-title":"Color Res. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:02:31Z","timestamp":1760130151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,7]]},"references-count":27,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23198297"],"URL":"https:\/\/doi.org\/10.3390\/s23198297","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,10,7]]}}}