{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:36:24Z","timestamp":1780511784322,"version":"3.54.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T00:00:00Z","timestamp":1686096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906177"],"award-info":[{"award-number":["61906177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019BF034"],"award-info":[{"award-number":["ZR2019BF034"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Significant Applied Technology Innovation Projects for Agriculture of Shandong Province","award":["SD2019NJ020"],"award-info":[{"award-number":["SD2019NJ020"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-15542-3","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T07:02:08Z","timestamp":1686121328000},"page":"7335-7361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Underwater video consistent enhancement: a real-world dataset and solution with progressive quality learning"],"prefix":"10.1007","volume":"83","author":[{"given":"Yongchang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunqian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dandan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,7]]},"reference":[{"key":"15542_CR1","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.asoc.2014.11.020","volume":"27","author":"AS Abdul Ghani","year":"2015","unstructured":"Abdul Ghani A S, Mat Isa N A (2015) Underwater image quality enhancement through integrated color model with rayleigh distribution. Appl Soft Comput 27:219\u2013230","journal-title":"Appl Soft Comput"},{"key":"15542_CR2","doi-asserted-by":"crossref","unstructured":"Akkaynak D, Treibitz T (2019) Sea-thru: a method for removing water from underwater images. In: IEEE Conference on computer vision and pattern recognition, pp 1682\u20131691","DOI":"10.1109\/CVPR.2019.00178"},{"key":"15542_CR3","doi-asserted-by":"crossref","unstructured":"Ancuti C, Ancuti C O, Haber T, Bekaert P (2012) Enhancing underwater images and videos by fusion. In: IEEE Conference on computer vision and pattern recognition, pp 81\u201388","DOI":"10.1109\/CVPR.2012.6247661"},{"key":"15542_CR4","first-page":"115978","volume":"89","author":"S Anwar","year":"2020","unstructured":"Anwar S, Li C (2020) Diving deeper into underwater image enhancement: a survey. Signal Process: Image Commun 89:115978","journal-title":"Signal Process: Image Commun"},{"key":"15542_CR5","unstructured":"Anwar S, Li C, Porikli F (2018) Deep underwater image enhancement. arXiv:1807.03528"},{"key":"15542_CR6","doi-asserted-by":"publisher","first-page":"128973","DOI":"10.1109\/ACCESS.2020.3009161","volume":"8","author":"L Bai","year":"2020","unstructured":"Bai L, Zhang W, Pan X, Zhao C (2020) Underwater image enhancement based on global and local equalization of histogram and dual-image multi-scale fusion. IEEE Access 8:128973\u2013128990","journal-title":"IEEE Access"},{"key":"15542_CR7","doi-asserted-by":"crossref","unstructured":"Berman D, Levy D, Avidan S, Treibitz T (2020) Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2020.2977624"},{"key":"15542_CR8","unstructured":"Berman D, Treibitz T, Avidan S (2017) Diving into haze-lines: color restoration of underwater images. In: British machine vision conference"},{"issue":"9","key":"15542_CR9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ecoinf.2013.10.006","volume":"23","author":"BJ Boom","year":"2014","unstructured":"Boom B J, He J, Palazzo S, Huang P X, Beyan C, Chou H-M, Lin F-P, Spampinato C, Fisher R B (2014) A research tool for long-term and continuous analysis of fish assemblage in coral-reefs using underwater camera footage. Eco Inform 23(9):83\u201397","journal-title":"Eco Inform"},{"issue":"1","key":"15542_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0016-0032(80)90058-7","volume":"310","author":"G Buchsbaum","year":"1980","unstructured":"Buchsbaum G (1980) A spatial processor model for object colour perception. J Franklin Inst 310(1):1\u201326","journal-title":"J Franklin Inst"},{"key":"15542_CR11","doi-asserted-by":"crossref","unstructured":"Cao K, Peng Y, Cosman P C (2018) Underwater image restoration using deep networks to estimate background light and scene depth. In: IEEE Southwest symposium on image analysis and interpretation, pp 1\u20134","DOI":"10.1109\/SSIAI.2018.8470347"},{"key":"15542_CR12","doi-asserted-by":"crossref","unstructured":"Carlevaris-Bianco N, Mohan A, Eustice R M (2010) Initial results in underwater single image dehazing. In: MTS\/IEEE SEATTLE, pp 1\u20138","DOI":"10.1109\/OCEANS.2010.5664428"},{"key":"15542_CR13","doi-asserted-by":"crossref","unstructured":"Chambah M, Semani D, Renouf A, Courtellemont P, Rizzi A (2003) Underwater color constancy: enhancement of automatic live fish recognition. In: Color imaging IX: processing, hardcopy, and applications, vol 5293, pp 157\u2013168","DOI":"10.1117\/12.524540"},{"issue":"4","key":"15542_CR14","doi-asserted-by":"publisher","first-page":"1756","DOI":"10.1109\/TIP.2011.2179666","volume":"21","author":"JY Chiang","year":"2012","unstructured":"Chiang J Y, Chen Y (2012) Underwater image enhancement by wavelength compensation and dehazing. IEEE Trans Image Process 21(4):1756\u20131769","journal-title":"IEEE Trans Image Process"},{"key":"15542_CR15","doi-asserted-by":"crossref","unstructured":"Cutter G, Stierhoff K, Zeng J (2015) Automated detection of rockfish in unconstrained underwater videos using haar cascades and a new image dataset: labeled fishes in the wild. In: IEEE Winter applications and computer vision workshops, pp 57\u201362","DOI":"10.1109\/WACVW.2015.11"},{"key":"15542_CR16","doi-asserted-by":"publisher","first-page":"105947","DOI":"10.1016\/j.optlastec.2019.105947","volume":"123","author":"C Dai","year":"2020","unstructured":"Dai C, Lin M, Wu X, Wang Z, Guan Z (2020) Single underwater image restoration by decomposing curves of attenuating color. Opt Laser Technol 123:105947","journal-title":"Opt Laser Technol"},{"issue":"2","key":"15542_CR17","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/TCYB.2020.2998481","volume":"52","author":"D Ding","year":"2022","unstructured":"Ding D, Wang W, Tong J, Gao X, Liu Z, Fang Y (2022) Biprediction-based video quality enhancement via learning. IEEE Trans Cybern 52(2):1207\u20131220","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"15542_CR18","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/MCG.2016.26","volume":"36","author":"PLJ Drews","year":"2016","unstructured":"Drews P L J, Nascimento E R, Botelho S S C, Montenegro Campos M F (2016) Underwater depth estimation and image restoration based on single images. IEEE Comput Graphics Appl 36(2):24\u201335","journal-title":"IEEE Comput Graphics Appl"},{"key":"15542_CR19","doi-asserted-by":"crossref","unstructured":"Fabbri C, Islam M J, Sattar J (2018) Enhancing underwater imagery using generative adversarial networks. In: IEEE International conference on robotics and automation, pp 7159\u20137165","DOI":"10.1109\/ICRA.2018.8460552"},{"key":"15542_CR20","doi-asserted-by":"crossref","unstructured":"Fu X, Fan Z, Ling M, Huang Y, Ding X (2017) Two-step approach for single underwater image enhancement. In: International symposium on intelligent signal processing and communication systems , pp 789\u2013794","DOI":"10.1109\/ISPACS.2017.8266583"},{"key":"15542_CR21","doi-asserted-by":"crossref","unstructured":"Fu X, Zhuang P, Huang Y, Liao Y, Zhang X, Ding X (2014) A retinex-based enhancing approach for single underwater image. In: IEEE International conference on image processing, pp 4572\u20134576","DOI":"10.1109\/ICIP.2014.7025927"},{"key":"15542_CR22","first-page":"115892","volume":"86","author":"X Fu","year":"2020","unstructured":"Fu X, Cao X (2020) Underwater image enhancement with global\u2013local networks and compressed-histogram equalization. Signal Process: Image Commun 86:115892","journal-title":"Signal Process: Image Commun"},{"key":"15542_CR23","doi-asserted-by":"crossref","unstructured":"Ghani A S A, Isa N A M (2014) Underwater image quality enhancement through composition of dual-intensity images and rayleigh-stretching. In: IEEE International conference on consumer electronics. Berlin, pp 219\u2013220","DOI":"10.1109\/ICCE-Berlin.2014.7034265"},{"issue":"3","key":"15542_CR24","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/TPAMI.2019.2944806","volume":"43","author":"Z Guan","year":"2021","unstructured":"Guan Z, Xing Q, Xu M, Yang R, Liu T, Wang Z (2021) Mfqe 2.0: a new approach for multi-frame quality enhancement on compressed video. IEEE Trans Pattern Anal Mach Intell 43(3):949\u2013963","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"15542_CR25","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","volume":"33","author":"K He","year":"2011","unstructured":"He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341\u20132353","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15542_CR26","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"15542_CR27","doi-asserted-by":"publisher","first-page":"122078","DOI":"10.1109\/ACCESS.2020.3006359","volume":"8","author":"G Hou","year":"2020","unstructured":"Hou G, Zhao X, Pan Z, Yang H, Tan L, Li J (2020) Benchmarking underwater image enhancement and restoration, and beyond. IEEE Access 8:122078\u2013122091","journal-title":"IEEE Access"},{"key":"15542_CR28","doi-asserted-by":"publisher","first-page":"102732","DOI":"10.1016\/j.jvcir.2019.102732","volume":"66","author":"G Hou","year":"2020","unstructured":"Hou G, Li J, Wang G, Yang H, Huang B, Pan Z (2020) A novel dark channel prior guided variational framework for underwater image restoration. J Vis Commun Image Represent 66:102732","journal-title":"J Vis Commun Image Represent"},{"key":"15542_CR29","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2019.08.041","volume":"369","author":"G Hou","year":"2019","unstructured":"Hou G, Pan Z, Wang G, Yang H, Duan J (2019) An efficient nonlocal variational method with application to underwater image restoration. Neurocomputing 369:106\u2013121","journal-title":"Neurocomputing"},{"key":"15542_CR30","doi-asserted-by":"crossref","unstructured":"Hou M, Liu R, Fan X, Luo Z (2018) Joint residual learning for underwater image enhancement. In: IEEE International conference on image processing, pp 4043\u20134047","DOI":"10.1109\/ICIP.2018.8451209"},{"key":"15542_CR31","doi-asserted-by":"publisher","first-page":"2152","DOI":"10.1109\/LSP.2021.3099746","volume":"28","author":"J Hu","year":"2021","unstructured":"Hu J, Jiang Q, Cong R, Gao W, Shao F (2021) Two-branch deep neural network for underwater image enhancement in hsv color space. IEEE Signal Process Lett 28:2152\u20132156","journal-title":"IEEE Signal Process Lett"},{"key":"15542_CR32","doi-asserted-by":"crossref","unstructured":"Huang D, Wang Y, Song W, Sequeira J, Mavromatis S (2018) Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition. In: MultiMedia Modeling, pp 453\u2013465","DOI":"10.1007\/978-3-319-73603-7_37"},{"key":"15542_CR33","doi-asserted-by":"crossref","unstructured":"Iqbal K, Odetayo M, James A, Salam RA, Talib AZH (2010) Enhancing the low quality images using unsupervised colour correction method. In: IEEE International conference on systems, man and cybernetics, pp 1703\u20131709","DOI":"10.1109\/ICSMC.2010.5642311"},{"issue":"2","key":"15542_CR34","doi-asserted-by":"publisher","first-page":"3227","DOI":"10.1109\/LRA.2020.2974710","volume":"5","author":"MJ Islam","year":"2020","unstructured":"Islam M J, Xia Y, Sattar J (2020) Fast underwater image enhancement for improved visual perception. IEEE Robot Autom Lett 5(2):3227\u20133234","journal-title":"IEEE Robot Autom Lett"},{"key":"15542_CR35","unstructured":"Islam M J, Luo P, Sattar J (2020) Simultaneous enhancement and super-resolution of underwater imagery for improved visual perception. In: Robotics: science and systems"},{"issue":"2","key":"15542_CR36","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/48.50695","volume":"15","author":"JS Jaffe","year":"1990","unstructured":"Jaffe J S (1990) Computer modeling and the design of optimal underwater imaging systems. IEEE J Oceanic Eng 15(2):101\u2013111","journal-title":"IEEE J Oceanic Eng"},{"key":"15542_CR37","doi-asserted-by":"crossref","unstructured":"Jian M, Qi Q, Dong J, Yin Y, Zhang W, Lam K (2017) The ouc-vision large-scale underwater image database. In: IEEE International conference on multimedia and expo, pp 1297\u20131302","DOI":"10.1109\/ICME.2017.8019324"},{"issue":"2","key":"15542_CR38","first-page":"239","volume":"34","author":"I Kashif","year":"2007","unstructured":"Kashif I, Salam R A, Azam O, Talib A Z (2007) Underwater image enhancement using an integrated colour model. Iaeng Int J Comput Sci 34(2):239\u2013244","journal-title":"Iaeng Int J Comput Sci"},{"key":"15542_CR39","unstructured":"Krizhevsky A, Sutskever I, Hinton G E (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"11","key":"15542_CR40","doi-asserted-by":"publisher","first-page":"15121","DOI":"10.1007\/s11042-018-6849-9","volume":"78","author":"N Kumar","year":"2019","unstructured":"Kumar N, Sardana H K, Shome S N (2019) Saliency based shape extraction of objects in unconstrained underwater environment. Multimed Tools Applic 78(11):15121\u201315139","journal-title":"Multimed Tools Applic"},{"key":"15542_CR41","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TIP.2019.2955241","volume":"29","author":"C Li","year":"2020","unstructured":"Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2020) An underwater image enhancement benchmark dataset and beyond. IEEE Trans Image Process 29:4376\u20134389","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"15542_CR42","doi-asserted-by":"publisher","first-page":"5664","DOI":"10.1109\/TIP.2016.2612882","volume":"25","author":"C Li","year":"2016","unstructured":"Li C, Guo J, Cong R, Pang Y, Wang B (2016) Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Trans Image Process 25(12):5664\u20135677","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"15542_CR43","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/LSP.2018.2792050","volume":"25","author":"C Li","year":"2018","unstructured":"Li C, Guo J, Guo C (2018) Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Process Lett 25(3):323\u2013327","journal-title":"IEEE Signal Process Lett"},{"key":"15542_CR44","doi-asserted-by":"crossref","unstructured":"Li C, Quo J, Pang Y, Chen S, Wang J (2016) Single underwater image restoration by blue-green channels dehazing and red channel correction. In: IEEE International conference on acoustics, speech and signal processing, pp 1731\u20131735","DOI":"10.1109\/ICASSP.2016.7471973"},{"key":"15542_CR45","doi-asserted-by":"publisher","first-page":"107038","DOI":"10.1016\/j.patcog.2019.107038","volume":"98","author":"C Li","year":"2020","unstructured":"Li C, Anwar S, Porikli F (2020) Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn 98:107038","journal-title":"Pattern Recogn"},{"key":"15542_CR46","unstructured":"Li H, Li J, Wang W (2019) A fusion adversarial underwater image enhancement network with a public test dataset. arXiv:1906.06819"},{"key":"15542_CR47","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.optlastec.2017.09.017","volume":"110","author":"Y Li","year":"2019","unstructured":"Li Y, Zhang Y, Xu X, He L, Serikawa S, Kim H (2019) Dust removal from high turbid underwater images using convolutional neural networks. Opti Laser Technol 110:2\u20136","journal-title":"Opti Laser Technol"},{"key":"15542_CR48","doi-asserted-by":"crossref","unstructured":"Lin W H, Zhong J X, Liu S, Li T, Li G (2020) Roimix: proposal-fusion among multiple images for underwater object detection. In: IEEE International conference on acoustics, speech and signal processing, pp 2588\u20132592","DOI":"10.1109\/ICASSP40776.2020.9053829"},{"issue":"12","key":"15542_CR49","doi-asserted-by":"publisher","first-page":"4861","DOI":"10.1109\/TCSVT.2019.2963772","volume":"30","author":"R Liu","year":"2020","unstructured":"Liu R, Fan X, Zhu M, Hou M, Luo Z (2020) Real-world underwater enhancement: challenges, benchmarks, and solutions under natural light. IEEE Trans Circuits Syst Video Technol 30(12):4861\u20134875","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"9","key":"15542_CR50","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/LGRS.2019.2950056","volume":"17","author":"X Liu","year":"2020","unstructured":"Liu X, Gao Z, Chen B M (2020) Mlfcgan: multilevel feature fusion-based conditional gan for underwater image color correction. IEEE Geosci Remote Sens Lett 17(9):1488\u20131492","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"15542_CR51","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1016\/j.neucom.2020.07.130","volume":"453","author":"X Liu","year":"2021","unstructured":"Liu X, Gao Z, Chen B M (2021) Ipmgan: integrating physical model and generative adversarial network for underwater image enhancement. Neurocomputing 453:538\u2013551","journal-title":"Neurocomputing"},{"key":"15542_CR52","unstructured":"Liu C, Meng W (2010) Removal of water scattering. In: International conference on computer engineering and technology, vol 2, pp V2\u201335\u2013V2\u201339"},{"key":"15542_CR53","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.optlastec.2018.05.048","volume":"110","author":"J Lu","year":"2019","unstructured":"Lu J, Li N, Zhang S, Yu Z, Zheng H, Zheng B (2019) Multi-scale adversarial network for underwater image restoration. Opt Laser Technol 110:105\u2013113","journal-title":"Opt Laser Technol"},{"key":"15542_CR54","unstructured":"M Uplavikar P, Wu Z, Wang Z (2019) All-in-one underwater image enhancement using domain-adversarial learning. In: IEEE Conference on computer vision and pattern recognition workshops"},{"key":"15542_CR55","unstructured":"McGlamery B L (1980) A computer model for underwater camera systems. In: Ocean Optics VI, vol 0208, pp 221\u2013231"},{"key":"15542_CR56","doi-asserted-by":"publisher","first-page":"105810","DOI":"10.1016\/j.asoc.2019.105810","volume":"85","author":"KZ Mohd Azmi","year":"2019","unstructured":"Mohd Azmi K Z, Abdul Ghani A S, Md Yusof Z, Ibrahim Z (2019) Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm. Appl Soft Comput 85:105810","journal-title":"Appl Soft Comput"},{"key":"15542_CR57","doi-asserted-by":"crossref","unstructured":"Oleari F, Kallasi F, Rizzini D L, Aleotti J, Caselli S (2015) An underwater stereo vision system: from design to deployment and dataset acquisition. In: OCEANS, pp 1\u20136","DOI":"10.1109\/OCEANS-Genova.2015.7271529"},{"issue":"3","key":"15542_CR58","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/JOE.2015.2469915","volume":"41","author":"K Panetta","year":"2016","unstructured":"Panetta K, Gao C, Agaian S (2016) Human-visual-system-inspired underwater image quality measures. IEEE J Oceanic Eng 41(3):541\u2013551","journal-title":"IEEE J Oceanic Eng"},{"issue":"5","key":"15542_CR59","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graphics Appl 21(5):34\u201341","journal-title":"IEEE Comput Graphics Appl"},{"key":"15542_CR60","doi-asserted-by":"crossref","unstructured":"Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: European conference on computer vision, pp 746\u2013760","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"15542_CR61","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image ecognition. In: International conference on learning representations"},{"issue":"24","key":"15542_CR62","doi-asserted-by":"publisher","first-page":"25679","DOI":"10.1007\/s11042-017-4459-6","volume":"76","author":"K Srividhya","year":"2017","unstructured":"Srividhya K, Ramya M M (2017) Accurate object recognition in the underwater images using learning algorithms and texture features. Multimed Tools Applic 76(24):25679\u201325695","journal-title":"Multimed Tools Applic"},{"issue":"5","key":"15542_CR63","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1007\/s11760-019-01439-y","volume":"13","author":"C Tang","year":"2019","unstructured":"Tang C, Von Lukas U F, Vahl M, Wang S, Wang Y, Tan M (2019) Efficient underwater image and video enhancement based on retinex. SIViP 13(5):1011\u20131018","journal-title":"SIViP"},{"key":"15542_CR64","doi-asserted-by":"crossref","unstructured":"Torres-M\u00e9ndez L A, Dudek G (2005) Color correction of underwater images for aquatic robot inspection. In: Energy minimization methods in computer vision and pattern recognition. Springer, Berlin, pp 60\u201373","DOI":"10.1007\/11585978_5"},{"issue":"2","key":"15542_CR65","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1109\/JOE.2004.836395","volume":"31","author":"E Trucco","year":"2006","unstructured":"Trucco E, Olmos-Antillon A T (2006) Self-tuning underwater image restoration. IEEE J Oceanic Eng 31(2):511\u2013519","journal-title":"IEEE J Oceanic Eng"},{"key":"15542_CR66","doi-asserted-by":"crossref","unstructured":"Wang Y, Wu B (2010) Fast clear single underwater image. In: International conference on computational intelligence and software engineering, pp 1\u20134","DOI":"10.1109\/CISE.2010.5677214"},{"key":"15542_CR67","doi-asserted-by":"publisher","first-page":"123638","DOI":"10.1109\/ACCESS.2019.2932611","volume":"7","author":"M Yang","year":"2019","unstructured":"Yang M, Hu J, Li C, Rohde G, Du Y, Hu K (2019) An in-depth survey of underwater image enhancement and restoration. IEEE Access 7:123638\u2013123657","journal-title":"IEEE Access"},{"issue":"12","key":"15542_CR68","doi-asserted-by":"publisher","first-page":"6062","DOI":"10.1109\/TIP.2015.2491020","volume":"24","author":"M Yang","year":"2015","unstructured":"Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24(12):6062\u20136071","journal-title":"IEEE Trans Image Process"},{"issue":"27\u201328","key":"15542_CR69","doi-asserted-by":"publisher","first-page":"20373","DOI":"10.1007\/s11042-020-08701-3","volume":"79","author":"H Yu","year":"2020","unstructured":"Yu H, Li X, Lou Q, Lei C, Liu Z (2020) Underwater image enhancement based on DCP and depth transmission map. Multimed Tools Applic 79 (27\u201328):20373\u201320390","journal-title":"Multimed Tools Applic"},{"key":"15542_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1609\/aaai.v32i1.12255","volume":"32","author":"K Zhou","year":"2018","unstructured":"Zhou K, Qiao Y, Xiang T (2018) Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward. Proceedings of the AAAI Conference on Artificial Intelligence 32:1","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"4","key":"15542_CR71","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"W Zhou","year":"2004","unstructured":"Zhou W, Bovik A C, Sheikh H R, Simoncelli E P (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"15542_CR72","doi-asserted-by":"crossref","unstructured":"Zhu J, Park T, Isola P, Efros A A (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International conference on computer vision, pp 2242\u20132251","DOI":"10.1109\/ICCV.2017.244"},{"key":"15542_CR73","doi-asserted-by":"crossref","unstructured":"Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graphics Gems, 474\u2013485","DOI":"10.1016\/B978-0-12-336156-1.50061-6"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15542-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15542-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15542-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T07:08:51Z","timestamp":1704697731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15542-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,7]]},"references-count":73,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["15542"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15542-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,7]]},"assertion":[{"value":"9 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}