{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:01:50Z","timestamp":1771952510783,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2018YFC0307905"],"award-info":[{"award-number":["2018YFC0307905"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972240"],"award-info":[{"award-number":["61972240"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation and learning-based underwater image enhancement method for coping with various degradation problems in underwater images. We first derive a simplified model for describing various degradation problems in underwater images, then propose a model-based image simulation method that can generate images with a wide range of parameter values. The proposed image simulation method also comes with an image-selection part, which helps to prune the simulation dataset so that it can serve as a training set for learning to enhance the targeted underwater images. Afterwards, we propose a convolutional neural network based on the encoder-decoder backbone to learn to enhance various underwater images from the simulated images. Experiments on simulated and real underwater images with different degradation problems demonstrate the effectiveness of the proposed underwater image simulation and enhancement method, and reveal the advantages of the proposed method in comparison with many state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/info13040187","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T13:39:51Z","timestamp":1649338791000},"page":"187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Model-Based Underwater Image Simulation and Learning-Based Underwater Image Enhancement Method"],"prefix":"10.3390","volume":"13","author":[{"given":"Yidan","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Huiping","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Kelin","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Jingchuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Xiangqian","family":"Quan","sequence":"additional","affiliation":[{"name":"Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4553","DOI":"10.1109\/TIP.2017.2716194","article-title":"A Closed-Form Solution to Single Underwater Camera Calibration Using Triple Wavelength Dispersion and Its Application to Single Camera 3D Reconstruction","volume":"26","author":"Chen","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1007\/s11036-018-1117-9","article-title":"Deep-Sea Organisms Tracking Using Dehazing and Deep Learning","volume":"25","author":"Lu","year":"2018","journal-title":"Mob. Netw. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.5194\/isprs-archives-XLII-2-W3-25-2017","article-title":"The Effect of Underwater Imagery Radiometry on 3D Reconstruction and Orthoimagery","volume":"XLII-2\/W3","author":"Agrafiotis","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","first-page":"1862","article-title":"A Feature Learning and Object Recognition Framework for Underwater Fish Images","volume":"25","author":"Chuang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.oceaneng.2012.04.006","article-title":"Vision-based object detection and tracking for autonomous navigation of underwater robots","volume":"48","author":"Lee","year":"2012","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"140233","DOI":"10.1109\/ACCESS.2019.2932130","article-title":"An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"123638","DOI":"10.1109\/ACCESS.2019.2932611","article-title":"An In-Depth Survey of Underwater Image Enhancement and Restoration","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/JOE.2018.2838939","article-title":"DeepCaustics: Classification and Removal of Caustics from Underwater Imagery","volume":"44","author":"Forbes","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Reggiannini, M., and Moroni, D. (2020). The Use of Saliency in Underwater Computer Vision: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13010022"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"107038","DOI":"10.1016\/j.patcog.2019.107038","article-title":"Underwater scene prior inspired deep underwater image and video enhancement","volume":"98","author":"Li","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4376","DOI":"10.1109\/TIP.2019.2955241","article-title":"An Underwater Image Enhancement Benchmark Dataset and Beyond","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_14","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_15","first-page":"239","article-title":"Underwater Image Enhancement Using an Integrated Colour Model","volume":"32","author":"Iqbal","year":"2007","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ancuti, C.O., Ancuti, C., Haber, T., and Bekaert, P. (2011, January 11\u201314). Fusion-based restoration of the underwater images. Proceedings of the 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6115744"},{"key":"ref_17","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":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X.-P., and Ding, X. (2014, January 27\u201330). A retinex-based enhancing approach for single underwater image. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025927"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, W., Li, G., and Ying, Z. (2017, January 10\u201313). A new underwater image enhancing method via color correction and illumination adjustment. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305027"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TIP.2017.2663846","article-title":"Underwater Image Restoration Based on Image Blurriness and Light Absorption","volume":"26","author":"Peng","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","article-title":"Single Image Haze Removal Using Dark Channel Prior","volume":"33","author":"He","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.jvcir.2014.11.006","article-title":"Automatic Red-Channel underwater image restoration","volume":"26","author":"Galdran","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Drews, P., do Nascimento, E., Moraes, F., Botelho, S., and Campos, M. (2013, January 2\u20138). Transmission Estimation in Underwater Single Images. Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops, Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.113"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Carlevaris-Bianco, N., Mohan, A., and Eustice, R.M. (2010, January 20\u201323). Initial results in underwater single image dehazing. Proceedings of the OCEANS 2010 MTS\/IEEE SEATTLE, Seattle, WA, USA.","DOI":"10.1109\/OCEANS.2010.5664428"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Y., Xu, H., Shang, D., Li, C., and Quan, X. (2019). An Underwater Image Enhancement Method for Different Illumination Conditions Based on Color Tone Correction and Fusion-Based Descattering. Sensors, 19.","DOI":"10.3390\/s19245567"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Marques, T.P., Albu, A.B., and Hoeberechts, M. (2019). A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images. J. Imaging, 5.","DOI":"10.3390\/jimaging5100079"},{"key":"ref_27","unstructured":"Uplavikar, P., Wu, Z., and Wang, Z. (2019). All-In-One Underwater Image Enhancement using Domain-Adversarial Learning. arXiv."},{"key":"ref_28","unstructured":"Wang, N., Zhou, Y., Han, F., Zhu, H., and Zheng, Y. (2019). UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazing. arXiv."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"4985","DOI":"10.1109\/TIP.2021.3076367","article-title":"Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fabbri, C., Islam, J., and Sattar, J. (2018, January 21\u201325). Enhancing Underwater Imagery Using Generative Adversarial Networks. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460552"},{"key":"ref_32","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, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_33","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":"2020","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/LGRS.2019.2950056","article-title":"MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction","volume":"17","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/48.50695","article-title":"Computer modeling and the design of optimal underwater imaging systems","volume":"15","author":"Jaffe","year":"1990","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1117\/12.958279","article-title":"A Computer Model for Underwater Camera Systems","volume":"208","author":"McGlamery","year":"1980","journal-title":"SPIE Proc."},{"key":"ref_37","unstructured":"Schechner, Y., and Karpel, N. (July, January 27). Clear underwater vision. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, Washington, DC, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/JOE.2005.850871","article-title":"Recovery of Underwater Visibility and Structure by Polarization Analysis","volume":"30","author":"Schechner","year":"2005","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Akkaynak, D., Treibitz, T., Shlesinger, T., Loya, Y., Tamir, R., and Iluz, D. (2017, January 21\u201326). What is the Space of Attenuation Coefficients in Underwater Computer Vision?. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.68"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.oceaneng.2014.11.036","article-title":"Deriving inherent optical properties from background color and underwater image enhancement","volume":"94","author":"Zhao","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_41","unstructured":"Mobley, C.D. (1994). Light and Water: Radiattive Transfer in Natural Waters, Academic Press."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Z., and Snavely, N. (2018, January 18\u201323). MegaDepth: Learning Single-View Depth Prediction from Internet Photos. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00218"},{"key":"ref_43","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance Normalization: The Missing Ingredient for Fast Stylization. arXiv."},{"key":"ref_44","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th ICML Workshop on Deep Learning for Audio, Speech and Language Processing, Atlanta, GA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss Functions for Image Restoration with Neural Networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_46","first-page":"740","article-title":"Microsoft COCO: Common Objects in Context","volume":"Volume 8693","author":"Lin","year":"2014","journal-title":"European Conference on Computer Vision, Proceedings of the 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014"},{"key":"ref_47","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_48","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":"2016","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, F., Wu, J., Wang, Y., Zhao, Y., and Zhang, X. (2012, January 18\u201320). A color cast detection algorithm of robust performance. Proceedings of the 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), Nanjing, China.","DOI":"10.1109\/ICACI.2012.6463249"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/4\/187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:49:32Z","timestamp":1760136572000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/4\/187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,7]]},"references-count":49,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["info13040187"],"URL":"https:\/\/doi.org\/10.3390\/info13040187","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,7]]}}}