{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T20:57:14Z","timestamp":1774299434280,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Commonwealth Scientific and Industrial Research Organisation (CSIRO)","award":["AIM FSP_TB07_WP05"],"award-info":[{"award-number":["AIM FSP_TB07_WP05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in recent decades. However, due to fundamental difficulties associated with imaging\/sensing, lighting, and refractive geometric distortions in capturing clear underwater images, no comprehensive evaluations have been conducted with regard to underwater image restoration. To address this gap, we constructed a large-scale real underwater image dataset, dubbed Heron Island Coral Reef Dataset (\u2018HICRD\u2019), for the purpose of benchmarking existing methods and supporting the development of new deep-learning based methods. We employed an accurate water parameter (diffuse attenuation coefficient) to generate the reference images. There are 2000 reference restored images and 6003 original underwater images in the unpaired training set. Furthermore, we present a novel method for underwater image restoration based on an unsupervised image-to-image translation framework. Our proposed method leveraged contrastive learning and generative adversarial networks to maximize the mutual information between raw and restored images. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method. Our code and dataset are both publicly available.<\/jats:p>","DOI":"10.3390\/rs14174297","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"4297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["Underwater Image Restoration via Contrastive Learning and a Real-World Dataset"],"prefix":"10.3390","volume":"14","author":[{"given":"Junlin","family":"Han","sequence":"first","affiliation":[{"name":"Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"},{"name":"College of Engineering & Computer Science, Australian National University, Canberra 2601, Australia"}]},{"given":"Mehrdad","family":"Shoeiby","sequence":"additional","affiliation":[{"name":"Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7161-8770","authenticated-orcid":false,"given":"Tim","family":"Malthus","sequence":"additional","affiliation":[{"name":"Oceans & Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-5373","authenticated-orcid":false,"given":"Elizabeth","family":"Botha","sequence":"additional","affiliation":[{"name":"Oceans & Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1681-9630","authenticated-orcid":false,"given":"Janet","family":"Anstee","sequence":"additional","affiliation":[{"name":"Oceans & Atmosphere, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"given":"Saeed","family":"Anwar","sequence":"additional","affiliation":[{"name":"Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"},{"name":"College of Engineering & Computer Science, Australian National University, Canberra 2601, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1727-728X","authenticated-orcid":false,"given":"Ran","family":"Wei","sequence":"additional","affiliation":[{"name":"Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"given":"Mohammad Ali","family":"Armin","sequence":"additional","affiliation":[{"name":"Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]},{"given":"Hongdong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering & Computer Science, Australian National University, Canberra 2601, Australia"}]},{"given":"Lars","family":"Petersson","sequence":"additional","affiliation":[{"name":"Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra 2601, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Reggiannini, M., and Moroni, D. (2021). The Use of Saliency in Underwater Computer Vision: A Review. Remote Sens., 13.","DOI":"10.3390\/rs13010022"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6284","DOI":"10.1109\/TGRS.2013.2295843","article-title":"Exploiting environmental information for improved underwater target classification in sonar imagery","volume":"52","author":"Williams","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ludeno, G., Capozzoli, L., Rizzo, E., Soldovieri, F., and Catapano, I. (2018). A microwave tomography strategy for underwater imaging via ground penetrating radar. Remote Sens., 10.","DOI":"10.3390\/rs10091410"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/TGRS.2014.2324971","article-title":"Contributions to automatic target recognition systems for underwater mine classification","volume":"53","author":"Fei","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Akkaynak, D., and Treibitz, T. (2018, January 18\u201323). A Revised Underwater Image Formation Model. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00703"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8117","DOI":"10.1109\/TGRS.2020.3033407","article-title":"An Underwater Image Vision Enhancement Algorithm Based on Contour Bougie Morphology","volume":"59","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","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_9","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, NSW, Australia.","DOI":"10.1109\/ICCVW.2013.113"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1109\/TIP.2011.2179666","article-title":"Underwater image enhancement by wavelength compensation and dehazing","volume":"21","author":"Chiang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1364\/JOSAA.32.000886","article-title":"Contrast enhancement for images in turbid water","volume":"32","author":"Lu","year":"2015","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_13","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_14","unstructured":"Jerlov, N.G. (1976). Marine Optics, Elsevier."},{"key":"ref_15","first-page":"2822","article-title":"Underwater single image color restoration using haze-lines and a new quantitative dataset","volume":"43","author":"Berman","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"12430","DOI":"10.1038\/s41598-018-30566-8","article-title":"Polarimetric image recovery method combining histogram stretching for underwater imaging","volume":"8","author":"Li","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106152","DOI":"10.1016\/j.optlaseng.2020.106152","article-title":"Polarimetric underwater image recovery via deep learning","volume":"133","author":"Hu","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cao, K., Peng, Y.T., and Cosman, P.C. (2018, January 8\u201310). Underwater image restoration using deep networks to estimate background light and scene depth. Proceedings of the 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Las Vegas, NV, USA.","DOI":"10.1109\/SSIAI.2018.8470347"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Barbosa, W.V., Amaral, H.G., Rocha, T.L., and Nascimento, E.R. (2018, January 7\u201310). Visual-quality-driven learning for underwater vision enhancement. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451356"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"Dehazenet: An end-to-end system for single image haze removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hou, M., Liu, R., Fan, X., and Luo, Z. (2018, January 7\u201310). Joint residual learning for underwater image enhancement. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451209"},{"key":"ref_23","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_24","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_25","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":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Duarte, A., Codevilla, F., Gaya, J.D.O., and Botelho, S.S. (2016, January 10\u201313). A dataset to evaluate underwater image restoration methods. Proceedings of the OCEANS 2016-Shanghai, Shanghai, China.","DOI":"10.1109\/OCEANSAP.2016.7485524"},{"key":"ref_27","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 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460552"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Wang, K., Hu, Y., Chen, J., Wu, X., Zhao, X., and Li, Y. (2019). Underwater image restoration based on a parallel convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11131591"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/LSP.2018.2792050","article-title":"Emerging from water: Underwater image color correction based on weakly supervised color transfer","volume":"25","author":"Li","year":"2018","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Silberman, N., Derek Hoiem, P.K., and Fergus, R. (2012, January 7\u201313). Indoor Segmentation and Support Inference from RGBD Images. Proceedings of the ECCV, Florence, Italy.","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Akkaynak, D., and Treibitz, T. (2019, January 15\u201320). Sea-Thru: A Method for Removing Water From Underwater Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00178"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"115978","DOI":"10.1016\/j.image.2020.115978","article-title":"Diving deeper into underwater image enhancement: A survey","volume":"89","author":"Anwar","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13\u201319). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref_35","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020, January 13\u201318). A simple framework for contrastive learning of visual representations. Proceedings of the International Conference on Machine Learning (ICML), Virtual Event."},{"key":"ref_36","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Han, J., Shoeiby, M., Malthus, T., Botha, E., Anstee, J., Anwar, S., Wei, R., Petersson, L., and Armin, M.A. (2021, January 11\u201316). Single Underwater Image Restoration by contrastive learning. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553857"},{"key":"ref_38","unstructured":"Salmond, J., Passenger, J., Kovacs, E., Roelfsema, C., and Stetner, D. (2018). Reef Check Australia 2018 Heron Island Reef Health Report, Reef Check Foundation Ltd."},{"key":"ref_39","unstructured":"Sch\u00f6nberg, C.H., and Suwa, R. (2007). Why bioeroding sponges may be better hosts for symbiotic dinoflagellates than many corals. Porifera Research: Biodiversity, Innovation and Sustainability, Museu Nacional."},{"key":"ref_40","unstructured":"Boss, E., Twardowski, M., McKee, D., Cetini\u0107, I., and Slade, W. (2019). Beam Transmission and Attenuation Coefficients: Instruments, Characterization, Field Measurements and Data Analysis Protocols, IOCCG. [2nd ed.]. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation;."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6024","DOI":"10.1002\/2014JC010205","article-title":"Impact of an extreme flood event on optical and biogeochemical properties in a subtropical coastal periurban embayment (Eastern Australia)","volume":"119","author":"Oubelkheir","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_42","unstructured":"Mannino, A., Novak, M.G., Nelson, N.B., Belz, M., Berthon, J.F., Blough, N.V., Boss, E., Brichaud, A., Chaves, J., and Del Castillo, C. (2019). Measurement Protocol of Absorption by Chromophoric Dissolved Organic Matter (CDOM) and Other Dissolved Materials, IOCCG. [1st ed.]. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gower, J.F.R. (1981). The Determination of the Diffuse Attenuation Coefficient of Sea Water Using the Coastal Zone Color Scanner. Oceanography from Space, Springer.","DOI":"10.1007\/978-1-4613-3315-9"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"30082","DOI":"10.1364\/OE.21.030082","article-title":"A new model for the vertical spectral diffuse attenuation coefficient of downwelling irradiance in turbid coastal waters: Validation with in situ measurements","volume":"21","author":"Simon","year":"2013","journal-title":"Opt. Express"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compeleceng.2013.10.016","article-title":"Underwater image dehazing using joint trilateral filter","volume":"40","author":"Serikawa","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_49","unstructured":"Park, T., Efros, A.A., Zhang, R., and Zhu, J.Y. (, January 8\u201314). Contrastive learning for unpaired image-to-image translation. Proceedings of the European Conference on Computer Vision, Munich, Germany."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"Han, J., Shoeiby, M., Petersson, L., and Armin, M.A. (2021, January 19\u201325). Dual Contrastive Learning for Unsupervised Image-to-Image Translation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00084"},{"key":"ref_52","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 Recognitio (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5664","DOI":"10.1109\/TIP.2016.2612882","article-title":"Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_55","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 International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7025927"},{"key":"ref_56","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_57","unstructured":"Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. arXiv."},{"key":"ref_58","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv."},{"key":"ref_59","unstructured":"Kingma, D.P., and Ba, J. (2014, January 14\u201316). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_60","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_61","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017, January 4\u20139). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Mangeruga, M., Bruno, F., Cozza, M., Agrafiotis, P., and Skarlatos, D. (2018). Guidelines for underwater image enhancement based on benchmarking of different methods. Remote Sens., 10.","DOI":"10.3390\/rs10101652"},{"key":"ref_63","unstructured":"Berman, D., Treibitz, T., and Avidan, S. (2017, January 4\u20137). Diving into haze-lines: Color restoration of underwater images. Proceedings of the British Machine Vision Conference (BMVC), London, UK."},{"key":"ref_64","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.68"},{"key":"ref_65","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_66","doi-asserted-by":"crossref","unstructured":"Yi, D.H., Gong, Z., Jech, J.M., Ratilal, P., and Makris, N.C. (2018). Instantaneous 3D continental-shelf scale imaging of oceanic fish by multi-spectral resonance sensing reveals group behavior during spawning migration. Remote Sens., 10.","DOI":"10.3390\/rs10010108"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Fu, X., Shang, X., Sun, X., Yu, H., Song, M., and Chang, C.I. (2020). Underwater hyperspectral target detection with band selection. Remote Sens., 12.","DOI":"10.3390\/rs12071056"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Mogstad, A.A., Johnsen, G., and Ludvigsen, M. (2019). Shallow-water habitat mapping using underwater hyperspectral imaging from an unmanned surface vehicle: A pilot study. Remote Sens., 11.","DOI":"10.3390\/rs11060685"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2947","DOI":"10.1109\/TGRS.2018.2878923","article-title":"Underwater hyperspectral imaging using a stationary platform in the Trans-Atlantic Geotraverse hydrothermal field","volume":"57","author":"Dumke","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"13101","DOI":"10.1364\/OE.24.013101","article-title":"Model-based restoration of underwater spectral images captured with narrowband filters","volume":"24","author":"Guo","year":"2016","journal-title":"Optics Express"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4297\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:21:19Z","timestamp":1760142079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,31]]},"references-count":70,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174297"],"URL":"https:\/\/doi.org\/10.3390\/rs14174297","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,31]]}}}