{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T18:48:10Z","timestamp":1769021290174,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2019YFB1311000"],"award-info":[{"award-number":["2019YFB1311000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013093","name":"Science and Technology Planning Project of Shenzhen Municipality","doi-asserted-by":"publisher","award":["212102210161"],"award-info":[{"award-number":["212102210161"]}],"id":[{"id":"10.13039\/501100013093","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012269","name":"Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province","doi-asserted-by":"publisher","award":["21A520013"],"award-info":[{"award-number":["21A520013"]}],"id":[{"id":"10.13039\/501100012269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010878","name":"State Administration for Science, Technology and Industry for National Defense","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010878","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-15658-6","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T18:02:05Z","timestamp":1687888925000},"page":"11127-11146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Underwater occluded object recognition with two-stage image reconstruction strategy"],"prefix":"10.1007","volume":"83","author":[{"given":"Jiyong","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8821-4550","authenticated-orcid":false,"given":"Tao","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Wantao","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Weishuo","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Cai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"issue":"8","key":"15658_CR1","first-page":"2822","volume":"43","author":"D Berman","year":"2020","unstructured":"Berman D, Levy D, Avidan S et al (2020) Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(8):2822\u20132837","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"15658_CR2","first-page":"898","volume":"2017","author":"D Bo","year":"2017","unstructured":"Bo D, Lin D (2017) Contrastive Learning for Image Captioning. IEEE\/CVF International Conference on Computer Vision and Pattern Recognition 2017:898\u2013907","journal-title":"IEEE\/CVF International Conference on Computer Vision and Pattern Recognition"},{"key":"15658_CR3","unstructured":"Bochkovskiy A, Wang CY, Mark Liao HY (2020) YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934"},{"key":"15658_CR4","first-page":"10","volume":"1","author":"L Cai","year":"2021","unstructured":"Cai L et al (2021) \u201cderwater Distortion Target Recognition Network (UDTRNet) via Enhanced Image Features.\". Comput Intell Neurosci 1:10","journal-title":"Comput Intell Neurosci"},{"key":"15658_CR5","doi-asserted-by":"crossref","unstructured":"Chen Z, Huang S, Tao D (2018) Context refinement for object detection. European Conference on Computer Vision 71\u201386","DOI":"10.1007\/978-3-030-01237-3_5"},{"key":"15658_CR6","doi-asserted-by":"crossref","unstructured":"Garcia R, Nicosevici T, Gracias N et al (2017) Exploring the seafloor with underwater robots: land, sea & air. Computer vision in vehicle technology 75\u201399","DOI":"10.1002\/9781118868065.ch4"},{"key":"15658_CR7","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. IEEE International Conference on Computer Vision 2015:1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"15658_CR8","first-page":"580","volume":"2014","author":"R Girshick","year":"2014","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2014:580\u2013587","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"15658_CR9","unstructured":"Goodfellow J, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Advances in neural information processing systems pp 2672\u20132680"},{"issue":"12","key":"15658_CR10","doi-asserted-by":"publisher","first-page":"13101","DOI":"10.1364\/OE.24.013101","volume":"24","author":"Y Guo","year":"2016","unstructured":"Guo Y, Song H, Liu H et al (2016) Model-based restoration of underwater spectral images captured with narrowband filters. Optics Express 24(12):13101\u20131312","journal-title":"Optics Express"},{"key":"15658_CR11","doi-asserted-by":"crossref","unstructured":"He Y, Zhu C, Wang, et al (2019) Bounding Box Regression With Uncertainty for Accurate Object Detection. 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2883\u20132892","DOI":"10.1109\/CVPR.2019.00300"},{"key":"15658_CR12","doi-asserted-by":"crossref","unstructured":"Huo G, Wu Z, Li J et al (2018) Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision. Sensors 8(18)","DOI":"10.3390\/s18103570"},{"issue":"6","key":"15658_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1729881418808991","volume":"15","author":"D Ji","year":"2018","unstructured":"Ji D, Li H, Chen CW et al (2018) Visual detection and feature recognition of underwater target using a novel model-based method. Int J Adv Robot Syst 15(6):1\u201310","journal-title":"Int J Adv Robot Syst"},{"key":"15658_CR14","doi-asserted-by":"publisher","first-page":"14859","DOI":"10.1007\/s11042-017-5070-6","volume":"77","author":"S Jia","year":"2018","unstructured":"Jia S, Zhang Y (2018) Saliency-based deep convolutional neural network for no-reference image quality assessment. Multimedia Tools Appl 77:14859\u201314872","journal-title":"Multimedia Tools Appl"},{"key":"15658_CR15","first-page":"5505","volume":"2018","author":"Y Jiahui","year":"2018","unstructured":"Jiahui Y, Zhe L, Jimei YX et al (2018) Generative image inpainting with contextual attention. IEEE Conference on Computer Vision and Pattern Recognition 2018:5505\u20135514","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"15658_CR16","first-page":"2960","volume":"2019","author":"W Jiaqi","year":"2019","unstructured":"Jiaqi W, Chen K, Yang S et al (2019) Region Proposal by Guided Anchoring. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019:2960\u20132969","journal-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"issue":"1","key":"15658_CR17","first-page":"201","volume":"21","author":"AG Kamalyan","year":"2002","unstructured":"Kamalyan AG, Simonyan VV (2002) On the number of solutions of a certain type of one-dimensional pseudodifferential equations in the Sobolev-Slobedetski space. J Shellfish Res 21(1):201\u2013210","journal-title":"J Shellfish Res"},{"key":"15658_CR18","unstructured":"Knausgrd KM, Wiklund A, Srdalen TK et al (2021) Temperate fish detection and classification: a deep learning based approach. Appl Intell 1\u201314"},{"key":"15658_CR19","unstructured":"Langis KD, Fulton M, Sattar J (2021) \u201dAn Analysis of Deep Object Detectors For Diver Detection.\u201d Retrieved from the Data Repository for the University of Minnesota"},{"key":"15658_CR20","doi-asserted-by":"publisher","first-page":"39273","DOI":"10.1109\/ACCESS.2020.2976121","volume":"8","author":"C Lei","year":"2020","unstructured":"Lei C, Qiankun S, Tao X, Yukun M, Zhenxue C (2020) Multi-AUV Collaborative Target Recognition Based on Transfer-Reinforcement Learning. IEEE Access 8:39273\u201339284","journal-title":"IEEE Access"},{"key":"15658_CR21","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TIP.2019.2955241","volume":"29","author":"C Li","year":"2019","unstructured":"Li C, Guo C, Ren W et al (2019) An Underwater Image Enhancement Benchmark Dataset and Beyond. IEEE Trans Image Process 29:4376\u20134389","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"15658_CR22","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TIP.2019.2955241","volume":"95","author":"C Li","year":"2020","unstructured":"Li C, Guo C, Ren W et al (2020) An Underwater Image Enhancement Benchmark Dataset and Beyond. IEEE Trans Image Process 95(12):4376\u20134389","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"15658_CR23","doi-asserted-by":"publisher","first-page":"313","DOI":"10.3390\/s21010313","volume":"21","author":"M Li","year":"2021","unstructured":"Li M, Mathai A, Lau SLH et al (2021) Underwater Object Detection and Reconstruction Based on Active Single-Pixel Imaging and Super-Resolution Convolutional Neural Network. Sensors 21(1):313","journal-title":"Sensors"},{"key":"15658_CR24","first-page":"21","volume":"2016","author":"W Liu","year":"2016","unstructured":"Liu W, Anguelov D, Erhan D et al (2016) Ssd: Single shot multibox detector. European Conference on Computer Vision (ECCV) 2016:21\u201337","journal-title":"European Conference on Computer Vision (ECCV)"},{"key":"15658_CR25","first-page":"4169","volume":"2020","author":"H Liu","year":"2020","unstructured":"Liu H, Jiang B, Xiao Y et al (2020) Coherent Semantic Attention for Image Inpainting. IEEE\/CVF International Conference on Computer Vision and Pattern Recognition 2020:4169\u20134178","journal-title":"IEEE\/CVF International Conference on Computer Vision and Pattern Recognition"},{"key":"15658_CR26","doi-asserted-by":"crossref","unstructured":"Li H, Yang X, Li ZM et al (2021) Underwater image enhancement with Image Colorfulness Measure. Signal Processing Image Communication. 95(10)","DOI":"10.1016\/j.image.2021.116225"},{"key":"15658_CR27","first-page":"2536","volume":"2016","author":"D Pathak","year":"2016","unstructured":"Pathak D, Krahenbuhl P, Donahue J et al (2016) Context encoders: Feature learning by inpainting. IEEE Conference on Computer Vision and Pattern Recognition 2016:2536\u20132544","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"issue":"2","key":"15658_CR28","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MS.2018.2886815","volume":"36","author":"A Pfeffer","year":"2019","unstructured":"Pfeffer A, Wu C, Fry G et al (2019) Software Adaptation for an Unmanned Undersea Vehicle. IEEE Software. 36(2):91\u201396","journal-title":"IEEE Software."},{"issue":"10","key":"15658_CR29","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1049\/iet-ipr.2019.0117","volume":"13","author":"JA Raihan","year":"2019","unstructured":"Raihan JA, Abas PE, De Silva LC (2019) Review of underwater image restoration algorithms. Iet Image Process 13(10):1587\u20131596","journal-title":"Iet Image Process"},{"key":"15658_CR30","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. 2016 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"key":"15658_CR31","unstructured":"Sanjeev A, Hrishikesh K, Mikhail K et al (2019) A Theoretical Analysis of Contrastive Unsupervised Representation Learning. International Conference on Machine Learning 9904\u20139923"},{"issue":"6","key":"15658_CR32","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"R Shaoqing","year":"2017","unstructured":"Shaoqing R, Kaiming H, Girshick R, Sun J (2017) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6):1137\u20131149","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"4","key":"15658_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JEI.29.4.043013","volume":"29","author":"T Shi","year":"2020","unstructured":"Shi T, Liu M, Niu Y et al (2020) Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm. J Electron Imaging 29(4):1\u201310","journal-title":"J Electron Imaging"},{"issue":"2","key":"15658_CR34","doi-asserted-by":"publisher","first-page":"5809","DOI":"10.1007\/s10489-020-02155-8","volume":"15","author":"AB Tamou","year":"2021","unstructured":"Tamou AB, Benzinou A (2021) Nasreddine K Multi-stream Fish Detection in Unconstrained Underwater Videos by the Fusion of Two Convolutional Neural Network Detectors. Appl Intell 15(2):5809\u20135821","journal-title":"Appl Intell"},{"issue":"8","key":"15658_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.OE.59.8.083102","volume":"59","author":"L Tengyue","year":"2020","unstructured":"Tengyue L, Shenghui R, Xueting C et al (2020) Underwater image enhancement framework and its application on an autonomous underwater vehicle platform. Opt Eng 59(8):1\u201312","journal-title":"Opt Eng"},{"key":"15658_CR36","unstructured":"Tianning Y, Wan F, Mengying F (2021) Multiple Instance Active Learning for Object Detection. Computer Vision and Pattern Recognition"},{"key":"15658_CR37","unstructured":"Tuanji W, Jianhua L, Yi L et al (2003) Image quality evaluation based on image weighted separating block peak signal to noise ratio. International Conference on Neural Networks & Signal Processing 994\u2013997"},{"key":"15658_CR38","doi-asserted-by":"crossref","unstructured":"Tuncel E, Ferhatosmanoglu H, Rose K (2002) VQ-Index: An Index Structure for Similarity Searching in Multimedia Databases. International Conference of Multimedia 543\u2013552","DOI":"10.1145\/641007.641117"},{"issue":"6","key":"15658_CR39","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.biosystemseng.2021.08.015","volume":"210","author":"D Wang","year":"2021","unstructured":"Wang D, He D (2021) Channel pruned YOLO V5s-based deep learning approach for rapid and accurate apple fruitlet detection before fruit thinning. Biosyst Eng 210(6):271\u2013281","journal-title":"Biosyst Eng"},{"issue":"2","key":"15658_CR40","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s11802-019-3858-x","volume":"18","author":"X Wang","year":"2019","unstructured":"Wang X, Ouyang J, Dayu LI et al (2019) Underwater Object Recognition Based on Deep Encoding-Decoding Network. J Ocean Univ China 18(2):120\u2013126","journal-title":"J Ocean Univ China"},{"key":"15658_CR41","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.margeo.2014.03.012","volume":"352","author":"RB Wynn","year":"2014","unstructured":"Wynn RB, Huvenne V, Murton BJ et al (2014) Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Marine Geol 352:451\u2013468","journal-title":"Marine Geol"},{"key":"15658_CR42","doi-asserted-by":"crossref","unstructured":"Yamashita A, Fujii M, Kaneko T (2007) Color registration of underwater images for underwater sensing with consideration of light attenuation. Proceedings of 2007 IEEE international conference on robotics and automation 4570\u20134575","DOI":"10.1109\/ROBOT.2007.364183"},{"key":"15658_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2021.3128269","volume":"99","author":"S Yang","year":"2021","unstructured":"Yang S, Wang J, Deng B et al (2021) Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing. IEEE Transactions on Neural Networks and Learning Systems 99:1\u201315","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"15658_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2021.3128269","volume":"99","author":"S Yang","year":"2021","unstructured":"Yang S, Wang J, Zhang N et al (2021) CerebelluMorphic: Large-Scale Neuromorphic Model and Architecture for Supervised Motor Learning. IEEE Transactions on Neural Networks and Learning Systems 99:1\u201315","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"15658_CR45","doi-asserted-by":"crossref","unstructured":"Yang S, Deng B, Wang J et al (2019) Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons. IEEE Transactions on Neural Networks and Learning Systems 1\u201315","DOI":"10.1109\/TNNLS.2019.2899936"},{"key":"15658_CR46","doi-asserted-by":"crossref","unstructured":"Yang S, Gao T, Wang J et al (2021) Efficient Spike-Driven Learning With Dendritic Event-Based Processing. Frontiers in neuroscience 15","DOI":"10.3389\/fnins.2021.601109"},{"key":"15658_CR47","doi-asserted-by":"crossref","unstructured":"Yu X, Xing X, Zheng H et al (2018) Man-made object recognition from underwater optical images using deep learning and transfer learning. IEEE international conference on acoustics, speech and signal processing (ICASSP) 1852\u20131856","DOI":"10.1109\/ICASSP.2018.8461549"},{"issue":"2","key":"15658_CR48","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/cgf.142624","volume":"40","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Aydin TO (2021) Deep HDR estimation with generative detail reconstruction. Comput Graphics Forum 40(2):179\u2013190","journal-title":"Comput Graphics Forum"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15658-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15658-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15658-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T09:45:46Z","timestamp":1704879946000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15658-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["15658"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15658-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]},"assertion":[{"value":"16 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflicts of interest regarding the publication of this paper. And The datasets generated and\/or analyzed during the present study are available from the corresponding author on reasonable request.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}}]}}