{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:51:45Z","timestamp":1777391505849,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-026-21250-5","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:40:08Z","timestamp":1769697608000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Concat U-net for multispectral satellite image enhancement with saliency preservation and skip connections"],"prefix":"10.1007","volume":"85","author":[{"given":"Poonam Rani","family":"Verma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9842-8125","authenticated-orcid":false,"given":"Ashish Kumar","family":"Bhandari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"21250_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2021.103203","volume":"118","author":"AK Bhandari","year":"2021","unstructured":"Bhandari AK, Srinivas K, Kumar A (2021) Optimized histogram computation model using cuckoo search for color image contrast distortion. Dig Signal Process 118:103203. https:\/\/doi.org\/10.1016\/j.dsp.2021.103203","journal-title":"Dig Signal Process"},{"key":"21250_CR2","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TCSVT.2017.2773461","volume":"29","author":"B-H Chen","year":"2019","unstructured":"Chen B-H, Wu Y-L, Shi L-F (2019) A fast image contrast enhancement algorithm using entropy-preserving mapping prior. IEEE Trans Circuits Syst Video Technol 29:38\u201349. https:\/\/doi.org\/10.1109\/TCSVT.2017.2773461","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"21250_CR3","doi-asserted-by":"publisher","unstructured":"Singh H, Kumar A, Balyan L, Singh G (2017) Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement. Comput Electr Eng 70. https:\/\/doi.org\/10.1016\/j.compeleceng.2017.06.029","DOI":"10.1016\/j.compeleceng.2017.06.029"},{"key":"21250_CR4","doi-asserted-by":"publisher","first-page":"6807","DOI":"10.1109\/TIM.2020.2976279","volume":"69","author":"AK Bhandari","year":"2020","unstructured":"Bhandari AK, Kandhway P, Maurya S (2020) Salp swarm algorithm-based optimally weighted histogram framework for image enhancement. IEEE Trans Instrum Meas 69:6807\u20136815. https:\/\/doi.org\/10.1109\/TIM.2020.2976279","journal-title":"IEEE Trans Instrum Meas"},{"key":"21250_CR5","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/LGRS.2009.2034873","volume":"7","author":"H Demirel","year":"2010","unstructured":"Demirel H, Ozcinar C, Anbarjafari G (2010) Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci Remote Sens Lett 7:333\u2013337. https:\/\/doi.org\/10.1109\/LGRS.2009.2034873","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"21250_CR6","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.1109\/TGRS.2019.2955967","volume":"58","author":"T Maeda","year":"2020","unstructured":"Maeda T (2020) Spatial resolution enhancement algorithm based on the Backus-Gilbert method and its application to GCOM-W AMSR2 data. IEEE Trans Geosci Remote Sens 58:2809\u20132816. https:\/\/doi.org\/10.1109\/TGRS.2019.2955967","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21250_CR7","doi-asserted-by":"publisher","first-page":"7053","DOI":"10.1007\/s00521-021-06858-y","volume":"34","author":"AK Bhandari","year":"2022","unstructured":"Bhandari AK (2022) Swarm-based optimally selected histogram computation system for image enhancement. Neural Comput & Applic 34:7053\u20137067. https:\/\/doi.org\/10.1007\/s00521-021-06858-y","journal-title":"Neural Comput & Applic"},{"key":"21250_CR8","doi-asserted-by":"publisher","unstructured":"Guo C, Li C, Guo J et al (2020) Zero-reference deep curve estimation for low-light image enhancement. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit:1777\u20131786. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00185","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"21250_CR9","unstructured":"Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement. arXiv Prepr arXiv180804560"},{"key":"21250_CR10","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1109\/JSTARS.2018.2812726","volume":"11","author":"M Qin","year":"2018","unstructured":"Qin M, Xie F, Li W et al (2018) Dehazing for multispectral remote sensing images based on a convolutional neural network with the residual architecture. IEEE J Sel Top Appl Earth Obs Remote Sens 11:1645\u20131655. https:\/\/doi.org\/10.1109\/JSTARS.2018.2812726","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"21250_CR11","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.1109\/JSTARS.2015.2417864","volume":"8","author":"V Syrris","year":"2015","unstructured":"Syrris V, Ferri S, Ehrlich D, Pesaresi M (2015) Image enhancement and feature extraction based on low-resolution satellite data. IEEE J Sel Top Appl Earth Obs Remote Sens 8:1986\u20131995. https:\/\/doi.org\/10.1109\/JSTARS.2015.2417864","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"21250_CR12","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","volume":"57","author":"K Jiang","year":"2019","unstructured":"Jiang K, Wang Z, Yi P et al (2019) Edge-enhanced GAN for remote sensing image superresolution. IEEE Trans Geosci Remote Sens 57:5799\u20135812. https:\/\/doi.org\/10.1109\/TGRS.2019.2902431","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"21250_CR13","doi-asserted-by":"crossref","unstructured":"Yi C, Zhao Y-Q, Chan JC-W (2019) Spectral Super-Resolution for Multispectral Image Based on Spectral and Spatial Strategies. \u6536\u5165: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. \u9875 851\u2013854","DOI":"10.1109\/IGARSS.2019.8898630"},{"key":"21250_CR14","doi-asserted-by":"publisher","first-page":"21301","DOI":"10.1007\/s00521-022-07612-8","volume":"34","author":"Y Qian","year":"2022","unstructured":"Qian Y, Jiang Z, He Y et al (2022) Multi-scale error feedback network for low-light image enhancement. Neural Comput Appl 34:21301\u201321317. https:\/\/doi.org\/10.1007\/s00521-022-07612-8","journal-title":"Neural Comput Appl"},{"key":"21250_CR15","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1007\/s00521-023-08968-1","volume":"36","author":"T Zhou","year":"2024","unstructured":"Zhou T, Gao L, Hua R et al (2024) Fine-grained image recognition method for digital media based on feature enhancement strategy. Neural Comput Appl 36:2323\u20132335. https:\/\/doi.org\/10.1007\/s00521-023-08968-1","journal-title":"Neural Comput Appl"},{"key":"21250_CR16","doi-asserted-by":"publisher","first-page":"7733","DOI":"10.1007\/s00521-021-06836-4","volume":"34","author":"H Zhu","year":"2022","unstructured":"Zhu H (2022) Low-light image enhancement network with decomposition and adaptive information fusion. Neural Comput Applic 34:7733\u20137748. https:\/\/doi.org\/10.1007\/s00521-021-06836-4","journal-title":"Neural Comput Applic"},{"key":"21250_CR17","doi-asserted-by":"publisher","unstructured":"Zhang D, Guo Y, Zhou J et al (2023) TANet: transmission and atmospheric light driven enhancement of underwater images. Expert Syst Appl:122693. https:\/\/doi.org\/10.1016\/j.eswa.2023.122693","DOI":"10.1016\/j.eswa.2023.122693"},{"key":"21250_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122546","volume":"240","author":"S Zhang","year":"2024","unstructured":"Zhang S, Zhao S, An D et al (2024) LiteEnhanceNet: a lightweight network for real-time single underwater image enhancement. Expert Syst Appl 240:122546. https:\/\/doi.org\/10.1016\/j.eswa.2023.122546","journal-title":"Expert Syst Appl"},{"key":"21250_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121666","volume":"237","author":"M Kas","year":"2024","unstructured":"Kas M, Chahi A, Kajo I, Ruichek Y (2024) DLL-GAN: degradation-level-based learnable adversarial loss for image enhancement. Expert Syst Appl 237:121666. https:\/\/doi.org\/10.1016\/j.eswa.2023.121666","journal-title":"Expert Syst Appl"},{"key":"21250_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115034","volume":"182","author":"R Khan","year":"2021","unstructured":"Khan R, Yang Y, Liu Q, Qaisar ZH (2021) Divide and conquer: ill-light image enhancement via hybrid deep network. Expert Syst Appl 182:115034. https:\/\/doi.org\/10.1016\/j.eswa.2021.115034","journal-title":"Expert Syst Appl"},{"key":"21250_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122576","volume":"241","author":"S Jin","year":"2024","unstructured":"Jin S, Xu X, Su Z et al (2024) MVCT image enhancement using reference-based encoder\u2013decoder convolutional neural network. Expert Syst Appl 241:122576. https:\/\/doi.org\/10.1016\/j.eswa.2023.122576","journal-title":"Expert Syst Appl"},{"key":"21250_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118920","volume":"213","author":"X Yang","year":"2023","unstructured":"Yang X, Gong J, Wu L et al (2023) Reference-free low-light image enhancement by associating hierarchical wavelet representations. Expert Syst Appl 213:118920. https:\/\/doi.org\/10.1016\/j.eswa.2022.118920","journal-title":"Expert Syst Appl"},{"key":"21250_CR23","unstructured":"Wei C, Wang W, Yang W, Liu J (2018) Deep retinex decomposition for low-light enhancement. Br Mach Vis Conf"},{"key":"21250_CR24","doi-asserted-by":"publisher","first-page":"4965","DOI":"10.1109\/TIP.2015.2474701","volume":"24","author":"X Fu","year":"2015","unstructured":"Fu X, Liao Y, Zeng D et al (2015) A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans Image Process 24:4965\u20134977. https:\/\/doi.org\/10.1109\/TIP.2015.2474701","journal-title":"IEEE Trans Image Process"},{"key":"21250_CR25","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1109\/TMM.2020.2969790","volume":"22","author":"S Hao","year":"2020","unstructured":"Hao S, Han X, Guo Y et al (2020) Low-light image enhancement with semi-decoupled decomposition. IEEE Trans Multimed 22:3025\u20133038. https:\/\/doi.org\/10.1109\/TMM.2020.2969790","journal-title":"IEEE Trans Multimed"},{"key":"21250_CR26","doi-asserted-by":"publisher","first-page":"4805","DOI":"10.1109\/JSTARS.2025.3526260","volume":"18","author":"A Sharifi","year":"2025","unstructured":"Sharifi A, Safari MM (2025) Enhancing the spatial resolution of Sentinel-2 images through super-resolution using transformer-based deep-learning models. IEEE J Sel Top Appl Earth Obs Remote Sens 18:4805\u20134820. https:\/\/doi.org\/10.1109\/JSTARS.2025.3526260","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"21250_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-10286-6","volume":"15","author":"Z Xu","year":"2025","unstructured":"Xu Z, Wang X, Huang K, Chen R (2025) Low resolution remote sensing object detection with fine grained enhancement and swin transformer. Sci Rep 15:1\u201318. https:\/\/doi.org\/10.1038\/s41598-025-10286-6","journal-title":"Sci Rep"},{"key":"21250_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCC65144.2025.11063844","volume-title":"2nd Int Conf Electron Comput Commun control Technol ICECCC 2025","author":"A Varghese","year":"2025","unstructured":"Varghese A, Sudhakar T, Joy HK (2025) Enhancing satellite imagery with GAN based cloud removal. In: 2nd Int Conf Electron Comput Commun control Technol ICECCC 2025. https:\/\/doi.org\/10.1109\/ICECCC65144.2025.11063844"},{"key":"21250_CR29","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1631\/FITEE.2400261","volume":"26","author":"Y Zhu","year":"2025","unstructured":"Zhu Y, Wang L, Yuan J, Guo Y (2025) A ground-based dataset and diffusion model for on-orbit low-light image enhancement. Front Inf Technol Electron Eng 26:1083\u20131098. https:\/\/doi.org\/10.1631\/FITEE.2400261","journal-title":"Front Inf Technol Electron Eng"},{"key":"21250_CR30","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. \u6536\u5165: international conference on medical image computing and computer-assisted intervention. \u9875 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"21250_CR31","unstructured":"Ruder S (2016) An overview of gradient descent optimization algorithms. 1\u201314"},{"key":"21250_CR32","first-page":"1","volume-title":"Skip connections matter: on the transferability of adversarial examples generated with ResNets","author":"D Wu","year":"2020","unstructured":"Wu D, Wang Y, Xia S-T et al (2020) Skip connections matter: on the transferability of adversarial examples generated with ResNets, pp 1\u201315"},{"key":"21250_CR33","volume-title":"The usc-sipi image database, signal and image processing institute of the university of southern California","author":"A Weber","year":"1997","unstructured":"Weber A (1997) The usc-sipi image database, signal and image processing institute of the university of southern California. URL http\/\/sipiuscedu\/services\/database"},{"key":"21250_CR34","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1002\/pro.3993","volume":"30","author":"AB Schroeder","year":"2021","unstructured":"Schroeder AB, Dobson ETA, Rueden CT et al (2021) The ImageJ ecosystem: open-source software for image visualization, processing, and analysis. Protein Sci 30:234\u2013249","journal-title":"Protein Sci"},{"key":"21250_CR35","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/TFUZZ.2019.2930028","volume":"28","author":"AK Bhandari","year":"2020","unstructured":"Bhandari AK, Shahnawazuddin S, Meena AK (2020) A novel fuzzy clustering-based histogram model for image contrast enhancement. IEEE Trans Fuzzy Syst 28:2009\u20132021","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"21250_CR36","doi-asserted-by":"crossref","unstructured":"Singh N, Bhandari AK (2021) Principal component analysis-based low-light. 70:","DOI":"10.1109\/TIM.2021.3096266"},{"key":"21250_CR37","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.physd.2007.03.001","volume":"228","author":"JM Amig\u00f3","year":"2007","unstructured":"Amig\u00f3 JM, Kocarev L, Tomovski I (2007) Discrete entropy. Phys D Nonlinear Phenom 228:77\u201385","journal-title":"Phys D Nonlinear Phenom"},{"key":"21250_CR38","first-page":"39","volume":"3","author":"AK Vishwakarma","year":"2012","unstructured":"Vishwakarma AK, Mishra A (2012) Color image enhancement techniques: a critical review. Indian J Comput Sci Eng 3:39\u201345","journal-title":"Indian J Comput Sci Eng"},{"key":"21250_CR39","unstructured":"Phanthuna N, Cheevasuvit F, Chitwong S (2009) Contrast enhancement for minimum mean brightness error from histogram partitioning. \u6536\u5165: Annual Conference on American Society for Photogrammetry and Remote Sensing (March 2009)"},{"key":"21250_CR40","unstructured":"Agaian SS, Lentz KP, Grigoryan AM (2014) <a New Measure of Image Enhancment.Pdf>"},{"key":"21250_CR41","doi-asserted-by":"crossref","unstructured":"Hasler D, Suesstrunk SE (2003) Measuring colorfulness in natural images. \u6536\u5165: human vision and electronic imaging VIII. \u9875 87\u201395","DOI":"10.1117\/12.477378"},{"key":"21250_CR42","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1109\/TCE.2013.6626251","volume":"59","author":"K Panetta","year":"2013","unstructured":"Panetta K, Gao C, Agaian S (2013) No reference color image contrast and quality measures. IEEE Trans Consum Electron 59:643\u2013651","journal-title":"IEEE Trans Consum Electron"},{"key":"21250_CR43","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-319-12286-1_39","volume":"329","author":"T Sun","year":"2015","unstructured":"Sun T, Zhu X, Pan JS et al (2015) No-reference image quality assessment in spatial domain. Adv Intell Syst Comput 329:381\u2013388. https:\/\/doi.org\/10.1007\/978-3-319-12286-1_39","journal-title":"Adv Intell Syst Comput"},{"key":"21250_CR44","unstructured":"Ndajah P, Kikuchi H, Yukawa M et al (2010) SSIM image quality metric for denoised images. Int Conf Vis Imaging Simul - Proc:53\u201357"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21250-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-026-21250-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-026-21250-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:40:13Z","timestamp":1769697613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-026-21250-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["21250"],"URL":"https:\/\/doi.org\/10.1007\/s11042-026-21250-5","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"9 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare that they have no conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"48"}}