{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T09:02:14Z","timestamp":1779958934543,"version":"3.53.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Ministry of education, humanities and social sciences research project","award":["23YJAZH156"],"award-info":[{"award-number":["23YJAZH156"]}]},{"name":"the University Youth Innovation Team Foundation of Shandong Province","award":["2022KJ319"],"award-info":[{"award-number":["2022KJ319"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s13042-026-03147-9","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T08:06:21Z","timestamp":1779955581000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From enhancement to segmentation: a two-stage ocean microalgae image processing framework"],"prefix":"10.1007","volume":"17","author":[{"given":"Gengkun","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazheng","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"3147_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jwpe.2025.107439","volume":"71","author":"S Jayakrishna","year":"2025","unstructured":"Jayakrishna S, Ganesh SS (2025) Dad-yolo as a novel computer vision tool to predict the environmental impact of harmful algae presence in contaminated river water employed for large-scale irrigation to agricultural field. J Water Process Eng 71:107439","journal-title":"J Water Process Eng"},{"issue":"14","key":"3147_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/w14142219","volume":"14","author":"AS Abdullah","year":"2022","unstructured":"Abdullah AS, Khan Z, Hussain A, Athar A, Kim H-C (2022) Computer vision based deep learning approach for the detection and classification of algae species using microscopic images. Water Basel 14(14):2219","journal-title":"Water Basel"},{"key":"3147_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.biortech.2022.128418","volume":"369","author":"JWR Chong","year":"2023","unstructured":"Chong JWR, Khoo KS, Chew KW, Vo D-V, Balakrishnan D, Banat F, Munawaroh HSH, Iwamoto K, Show PL (2023) Microalgae identification: future of image processing and digital algorithm. Bioresour Technol 369:128418","journal-title":"Bioresour Technol"},{"key":"3147_CR4","first-page":"1","volume":"60","author":"Y Guo","year":"2022","unstructured":"Guo Y, Gao L, Li X (2022) A deep learning model for green algae detection on SAR images. IEEE Trans Geosci Remote Sens 60:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"3147_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110315","volume":"141","author":"F Barrientos-Espillco","year":"2023","unstructured":"Barrientos-Espillco F, Gasc\u00f3 E, L\u00f3pez-Gonz\u00e1lez CI, G\u00f3mez-Silva MJ, Pajares G (2023) Semantic segmentation based on deep learning for the detection of cyanobacterial harmful algal blooms (cyanohabs) using synthetic images. Appl Soft Comput 141:110315","journal-title":"Appl Soft Comput"},{"key":"3147_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2022.168899","volume":"259","author":"A Paul","year":"2022","unstructured":"Paul A, Bhattacharya P, Maity SP (2022) Histogram modification in adaptive bi-histogram equalization for contrast enhancement on digital images. Optik 259:168899","journal-title":"Optik"},{"key":"3147_CR7","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.procs.2022.12.111","volume":"216","author":"M Hayati","year":"2023","unstructured":"Hayati M, Muchtar K, Maulina N, Syamsuddin I, Elwirehardja GN, Pardamean B (2023) Impact of clahe-based image enhancement for diabetic retinopathy classification through deep learning. Procedia Comput Sci 216:57\u201366","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"3147_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-21745-9","volume":"12","author":"Y Song","year":"2022","unstructured":"Song Y, Li C, Xiao S, Xiao H, Guo B (2022) Unsharp masking image enhancement the parallel algorithm based on cross-platform. Sci Rep 12(1):20175","journal-title":"Sci Rep"},{"issue":"1","key":"3147_CR9","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TIP.2017.2759252","volume":"27","author":"CO Ancuti","year":"2017","unstructured":"Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2017) Color balance and fusion for underwater image enhancement. IEEE Trans Image Process 27(1):379\u2013393","journal-title":"IEEE Trans Image Process"},{"key":"3147_CR10","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: 24th International Conference, MMM 2018, Bangkok, Thailand, February 5-7, 2018, Proceedings, Part I 24, pp. 453\u2013465. Springer","DOI":"10.1007\/978-3-319-73603-7_37"},{"key":"3147_CR11","doi-asserted-by":"crossref","unstructured":"Fazal S, Khan D (2021) Underwater image enhancement using bi-histogram equalization with fuzzy plateau limit. In: 2021 7th International Conference on Signal Processing and Communication (ICSC), pp. 261\u2013266. IEEE","DOI":"10.1109\/ICSC53193.2021.9673286"},{"issue":"1","key":"3147_CR12","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1049\/iet-ipr.2016.0543","volume":"11","author":"X Liu","year":"2017","unstructured":"Liu X, Zhong G, Liu C, Dong J (2017) Underwater image colour constancy based on dsnmf. IET Image Process 11(1):38\u201343","journal-title":"IET Image Process"},{"issue":"3","key":"3147_CR13","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/JOE.2022.3140563","volume":"47","author":"W Zhang","year":"2022","unstructured":"Zhang W, Wang Y, Li C (2022) Underwater image enhancement by attenuated color channel correction and detail preserved contrast enhancement. IEEE J Oceanic Eng 47(3):718\u2013735","journal-title":"IEEE J Oceanic Eng"},{"key":"3147_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122710","volume":"242","author":"Y Wang","year":"2024","unstructured":"Wang Y, Hu S, Yin S, Deng Z, Yang Y-H (2024) A multi-level wavelet-based underwater image enhancement network with color compensation prior. Expert Syst Appl 242:122710","journal-title":"Expert Syst Appl"},{"key":"3147_CR15","doi-asserted-by":"crossref","unstructured":"Nezla N, Haridas TM, Supriya M (2021) Semantic segmentation of underwater images using unet architecture based deep convolutional encoder decoder model. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 28\u201333. IEEE","DOI":"10.1109\/ICACCS51430.2021.9441804"},{"key":"3147_CR16","doi-asserted-by":"crossref","unstructured":"Islam MJ, Edge C, Xiao Y, Luo P, Mehtaz M, Morse C, Enan SS, Sattar J (2020) Semantic segmentation of underwater imagery: Dataset and benchmark. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1769\u20131776. IEEE","DOI":"10.1109\/IROS45743.2020.9340821"},{"issue":"3","key":"3147_CR17","doi-asserted-by":"publisher","first-page":"188","DOI":"10.3390\/jmse8030188","volume":"8","author":"F Liu","year":"2020","unstructured":"Liu F, Fang M (2020) Semantic segmentation of underwater images based on improved deeplab. J Mar Sci Eng 8(3):188","journal-title":"J Mar Sci Eng"},{"key":"3147_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10499-024-01439-x","author":"Z He","year":"2024","unstructured":"He Z, Cao L, Luo J, Xu X, Tang J, Xu J, Xu G, Chen Z (2024) Uiss-net: underwater image semantic segmentation network for improving boundary segmentation accuracy of underwater images. Aquacult Int. https:\/\/doi.org\/10.1007\/s10499-024-01439-x","journal-title":"Aquacult Int"},{"key":"3147_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2024.106170","volume":"181","author":"D Li","year":"2024","unstructured":"Li D, Yang Y, Zhao S, Ding J (2024) Segmentation of underwater fish in complex aquaculture environments using enhanced soft attention mechanism. Environ Model Softw 181:106170","journal-title":"Environ Model Softw"},{"key":"3147_CR20","doi-asserted-by":"publisher","first-page":"33652","DOI":"10.1109\/ACCESS.2023.3262649","volume":"11","author":"M Chicchon","year":"2023","unstructured":"Chicchon M, Bedon H, Del-Blanco CR, Sipiran I (2023) Semantic segmentation of fish and underwater environments using deep convolutional neural networks and learned active contours. IEEE Access 11:33652\u201333665","journal-title":"IEEE Access"},{"key":"3147_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2024.102460","volume":"23","author":"S Pavithra","year":"2024","unstructured":"Pavithra S et al (2024) An efficient approach to detect and segment underwater images using swin transformer. Results Eng 23:102460","journal-title":"Results Eng"},{"issue":"2","key":"3147_CR22","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1109\/TCYB.2019.2925015","volume":"51","author":"W Liu","year":"2019","unstructured":"Liu W, Wang Z, Yuan Y, Zeng N, Hone K, Liu X (2019) A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Trans Cybern 51(2):1085\u20131093","journal-title":"IEEE Trans Cybern"},{"issue":"6","key":"3147_CR23","doi-asserted-by":"publisher","first-page":"16657","DOI":"10.1007\/s11042-023-16258-0","volume":"83","author":"G-K Wu","year":"2024","unstructured":"Wu G-K, Xu J, Zhang Y-D, Zhang B-P (2024) Underwater enhancement computing of ocean habs based on cyclic color compensation and multi-scale fusion. Multim Tools Appl 83(6):16657\u201316681","journal-title":"Multim Tools Appl"},{"key":"3147_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2022.116797","volume":"107","author":"Y Huang","year":"2022","unstructured":"Huang Y, Yuan F, Xiao F, Cheng E (2022) Underwater image enhancement based on color restoration and dual image wavelet fusion. Signal Process Image Commun 107:116797","journal-title":"Signal Process Image Commun"},{"issue":"16","key":"3147_CR25","doi-asserted-by":"publisher","first-page":"19338","DOI":"10.1007\/s10489-023-04502-x","volume":"53","author":"G-K Wu","year":"2023","unstructured":"Wu G-K, Zhang B-P, Xu J (2023) Numerical computation of ocean habs image enhancement based on empirical mode decomposition and wavelet fusion. Appl Intell 53(16):19338\u201319355","journal-title":"Appl Intell"},{"key":"3147_CR26","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.neucom.2022.04.051","volume":"493","author":"J Huang","year":"2022","unstructured":"Huang J, Fang Y, Wu Y, Wu H, Gao Z, Li Y, Del Ser J, Xia J, Yang G (2022) Swin transformer for fast mri. Neurocomputing 493:281\u2013304","journal-title":"Neurocomputing"},{"key":"3147_CR27","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.jmsy.2022.02.009","volume":"63","author":"J Yan","year":"2022","unstructured":"Yan J, Wang Z (2022) Yolo v3+ vgg16-based automatic operations monitoring and analysis in a manufacturing workshop under Industry 4.0. J Manuf Syst 63:134\u2013142","journal-title":"J Manuf Syst"},{"key":"3147_CR28","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.optlastec.2018.06.061","volume":"110","author":"S Sahu","year":"2019","unstructured":"Sahu S, Singh AK, Ghrera S, Elhoseny M et al (2019) An approach for de-noising and contrast enhancement of retinal fundus image using clahe. Opt Laser Technol 110:87\u201398","journal-title":"Opt Laser Technol"},{"key":"3147_CR29","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1109\/TIP.2019.2952032","volume":"29","author":"A Golts","year":"2019","unstructured":"Golts A, Freedman D, Elad M (2019) Unsupervised single image dehazing using dark channel prior loss. IEEE Trans Image Process 29:2692\u20132701","journal-title":"IEEE Trans Image Process"},{"issue":"12","key":"3147_CR30","doi-asserted-by":"publisher","first-page":"19478","DOI":"10.1364\/OE.492176","volume":"31","author":"H Song","year":"2023","unstructured":"Song H, Kong L (2023) Mask information-based gamma correction in fringe projection profilometry. Opt Express 31(12):19478\u201319490","journal-title":"Opt Express"},{"key":"3147_CR31","doi-asserted-by":"crossref","unstructured":"Wang S, Wang D, Wang E (2022) Improved underwater image enhancement model based on atomization images model and deep learning. In: 2022 IEEE 5th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), pp. 747\u2013752. IEEE","DOI":"10.1109\/AUTEEE56487.2022.9994539"},{"key":"3147_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Yang J, Ruan P, Wang P (2021) An improved unsupervised color correction algorithm for underwater image. In: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), pp. 1215\u20131220. IEEE","DOI":"10.1109\/ICIEA51954.2021.9516076"},{"key":"3147_CR33","doi-asserted-by":"crossref","unstructured":"Song W, Wang Y, Huang D, Tjondronegoro D (2018) A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration. In: Advances in Multimedia Information Processing\u2013PCM 2018: 19th Pacific-Rim Conference on Multimedia, Hefei, China, September 21-22, 2018, Proceedings, Part I 19, pp. 678\u2013688. Springer","DOI":"10.1007\/978-3-030-00776-8_62"},{"issue":"4","key":"3147_CR34","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1109\/TIP.2017.2663846","volume":"26","author":"Y-T Peng","year":"2017","unstructured":"Peng Y-T, Cosman PC (2017) Underwater image restoration based on image blurriness and light absorption. IEEE Trans Image Process 26(4):1579\u20131594","journal-title":"IEEE Trans Image Process"},{"key":"3147_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-019-2757-1","volume":"63","author":"G Zhai","year":"2020","unstructured":"Zhai G, Min X (2020) Perceptual image quality assessment: a survey. Sci China Inf Sci 63:1\u201352","journal-title":"Sci China Inf Sci"},{"key":"3147_CR36","doi-asserted-by":"publisher","first-page":"1980","DOI":"10.1109\/TMM.2021.3074825","volume":"24","author":"P Guo","year":"2021","unstructured":"Guo P, He L, Liu S, Zeng D, Liu H (2021) Underwater image quality assessment: subjective and objective methods. IEEE Trans Multimedia 24:1980\u20131989","journal-title":"IEEE Trans Multimedia"},{"key":"3147_CR37","doi-asserted-by":"publisher","first-page":"5093","DOI":"10.1109\/TMM.2022.3187212","volume":"25","author":"P Guo","year":"2022","unstructured":"Guo P, Liu H, Zeng D, Xiang T, Li L, Gu K (2022) An underwater image quality assessment metric. IEEE Trans Multimedia 25:5093\u20135106","journal-title":"IEEE Trans Multimedia"},{"key":"3147_CR38","unstructured":"Poma XS, Riba E, Sappa A (2020) Dense extreme inception network: Towards a robust cnn model for edge detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1923\u20131932"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03147-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03147-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03147-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T08:06:31Z","timestamp":1779955591000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03147-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":38,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["3147"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03147-9","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"10 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"326"}}