{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T03:57:46Z","timestamp":1777089466356,"version":"3.51.4"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"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":"crossref","award":["62102373"],"award-info":[{"award-number":["62102373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62102373"],"award-info":[{"award-number":["62102373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01500-6","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T07:38:32Z","timestamp":1735544312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dual-domain prior unfolding network for remote sensing image super-resolution"],"prefix":"10.1007","volume":"18","author":[{"given":"Jing","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guifu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"issue":"6","key":"1500_CR1","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TIP.2014.2305844","volume":"23","author":"T Peleg","year":"2014","unstructured":"Peleg T, Elad M (2014) A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans Image Process 23(6):2569\u20132582","journal-title":"IEEE Trans Image Process"},{"key":"1500_CR2","doi-asserted-by":"crossref","unstructured":"Dai D, Wang Y, Chen Y, Van\u00a0Gool L (2016) Is image super-resolution helpful for other vision tasks? In: 2016 IEEE Winter conference on applications of computer vision (WACV), pp 1\u20139","DOI":"10.1109\/WACV.2016.7477613"},{"key":"1500_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2023.109334","volume":"163","author":"J Zhang","year":"2023","unstructured":"Zhang J, Wang F, Zhang H, Shi X (2023) A novel CS 2g-starlet denoising method for high noise astronomical image. Opt Laser Technol 163:109334","journal-title":"Opt Laser Technol"},{"issue":"2","key":"1500_CR4","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/s00371-023-02842-w","volume":"40","author":"J Zhang","year":"2024","unstructured":"Zhang J, Wang F, Zhang H, Shi X (2024) Compressive sensing spatially adaptive total variation method for high-noise astronomical image denoising. Vis Comput 40(2):1215\u20131227","journal-title":"Vis Comput"},{"key":"1500_CR5","doi-asserted-by":"crossref","unstructured":"Zhou G, Tang Y, Zhang W, Liu W, Jiang Y, Gao E, Zhu Q, Bai Y (2023) shadow detection on high-resolution digital orthophoto map (dom) using semantic matching. IEEE Transactions on geoscience and remote sensing","DOI":"10.1109\/TGRS.2023.3294531"},{"issue":"8","key":"1500_CR6","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.3390\/rs15082208","volume":"15","author":"SD Khan","year":"2023","unstructured":"Khan SD, Basalamah S (2023) multi-scale and context-aware framework for flood segmentation in post-disaster high resolution aerial images. Remote Sensing 15(8):2208","journal-title":"Remote Sensing"},{"key":"1500_CR7","volume":"132","author":"P Yan","year":"2024","unstructured":"Yan P, Zhao J, Hou R, Duan X, Cai S, Wang X (2024) clustered remote sensing target distribution detection aided by density-based spatial analysis. Int J Appl Earth Obs Geoinf 132:104019","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"1500_CR8","first-page":"1","volume":"60","author":"G Zhou","year":"2022","unstructured":"Zhou G, Liu W, Zhu Q, Lu Y, Liu Y (2022) eca-mobilenetv3 (large)+ segnet model for binary sugarcane classification of remotely sensed images. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"1500_CR9","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/TGRS.2011.2160183","volume":"50","author":"XX Zhu","year":"2011","unstructured":"Zhu XX, Bamler R (2011) Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic sar. IEEE Trans Geosci Remote Sens 50(1):247\u2013258","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"1500_CR10","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TCSVT.2011.2163447","volume":"22","author":"Q Yuan","year":"2011","unstructured":"Yuan Q, Zhang L, Shen H (2011) Multiframe super-resolution employing a spatially weighted total variation model. IEEE Trans Circuits Syst Video Technol 22(3):379\u2013392","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"1500_CR11","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.image.2014.01.002","volume":"29","author":"J Lu","year":"2014","unstructured":"Lu J, Zhang H, Sun Y (2014) Video super resolution based on non-local regularization and reliable motion estimation. Signal Process Image Commun 29(4):514\u2013529","journal-title":"Signal Process Image Commun"},{"key":"1500_CR12","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: Proceedings of the European conference on computer vision (ECCV), Springer, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"issue":"1","key":"1500_CR13","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11263-022-01699-1","volume":"131","author":"M Zhou","year":"2023","unstructured":"Zhou M, Yan K, Pan J, Ren W, Xie Q, Cao X (2023) Memory-augmented deep unfolding network for guided image super-resolution. Int J Comput Vision 131(1):215\u2013242","journal-title":"Int J Comput Vision"},{"issue":"10","key":"1500_CR14","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/83.951537","volume":"10","author":"X Li","year":"2001","unstructured":"Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521\u20131527","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"1500_CR15","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1109\/TIP.2004.826093","volume":"13","author":"T Blu","year":"2004","unstructured":"Blu T, Th\u00e9venaz P, Unser M (2004) Linear interpolation revitalized. IEEE Trans Image Process 13(5):710\u2013719","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"1500_CR16","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/LSP.2011.2178595","volume":"19","author":"J Chen","year":"2011","unstructured":"Chen J, Nunez-Yanez J, Achim A (2011) Video super-resolution using generalized gaussian markov random fields. IEEE Signal Process Lett 19(2):63\u201366","journal-title":"IEEE Signal Process Lett"},{"issue":"12","key":"1500_CR17","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1109\/TIP.2006.881971","volume":"15","author":"R Pan","year":"2006","unstructured":"Pan R, Reeves SJ (2006) Efficient huber-markov edge-preserving image restoration. IEEE Trans Image Process 15(12):3728\u20133735","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"1500_CR18","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1137\/080725891","volume":"2","author":"T Goldstein","year":"2009","unstructured":"Goldstein T, Osher S (2009) The split bregman method for l1-regularized problems. SIAM J Imag Sci 2(2):323\u2013343","journal-title":"SIAM J Imag Sci"},{"key":"1500_CR19","doi-asserted-by":"crossref","unstructured":"Bioucas-Dias JM, Figueiredo MA, Oliveira JP (2006) Total variation-based image deconvolution: a majorization-minimization approach. In: 2006 IEEE International conference on acoustics speech and signal processing proceedings, vol 2","DOI":"10.1109\/ICASSP.2006.1660479"},{"issue":"3","key":"1500_CR20","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1137\/090746379","volume":"3","author":"X Zhang","year":"2010","unstructured":"Zhang X, Burger M, Bresson X, Osher S (2010) Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J Imag Sci 3(3):253\u2013276","journal-title":"SIAM J Imag Sci"},{"key":"1500_CR21","doi-asserted-by":"crossref","unstructured":"Lepcha DC, Goyal B, Dogra A, Goyal V (2022) Image super-resolution: A comprehensive review, recent trends, challenges and applications. Inform Fusion","DOI":"10.1016\/j.inffus.2022.10.007"},{"issue":"1","key":"1500_CR22","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1080\/01431161.2016.1264027","volume":"38","author":"R Fernandez-Beltran","year":"2017","unstructured":"Fernandez-Beltran R, Latorre-Carmona P, Pla F (2017) Single-frame super-resolution in remote sensing: A practical overview. Int J Remote Sens 38(1):314\u2013354","journal-title":"Int J Remote Sens"},{"key":"1500_CR23","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu\u00a0Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1500_CR24","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"},{"key":"1500_CR25","doi-asserted-by":"crossref","unstructured":"Choi J-S, Kim M (2017) A deep convolutional neural network with selection units for super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 154\u2013160","DOI":"10.1109\/CVPRW.2017.153"},{"key":"1500_CR26","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1500_CR27","doi-asserted-by":"crossref","unstructured":"Lu Z, Li J, Liu H, Huang C, Zhang L, Zeng T (2022) Transformer for single image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 457\u2013466","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"1500_CR28","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TCI.2020.2964202","volume":"6","author":"Y Li","year":"2020","unstructured":"Li Y, Tofighi M, Geng J, Monga V, Eldar YC (2020) Efficient and interpretable deep blind image deblurring via algorithm unrolling. IEEE Transactions on computational imaging 6:666\u2013681","journal-title":"IEEE Transactions on computational imaging"},{"key":"1500_CR29","doi-asserted-by":"crossref","unstructured":"Yang D, Sun J (2018) Proximal dehaze-net: A prior learning-based deep network for single image dehazing. In: Proceedings of the European conference on computer vision (ECCV), pp 702\u2013717","DOI":"10.1007\/978-3-030-01234-2_43"},{"key":"1500_CR30","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.1109\/TIP.2021.3069296","volume":"30","author":"H Van Luong","year":"2021","unstructured":"Van Luong H, Joukovsky B, Deligiannis N (2021) Designing interpretable recurrent neural networks for video reconstruction via deep unfolding. IEEE Trans Image Process 30:4099\u20134113","journal-title":"IEEE Trans Image Process"},{"key":"1500_CR31","unstructured":"Gregor K, LeCun Y (2010) Learning fast approximations of sparse coding. In: Proceedings of the 27th international conference on machine learning (ICML), pp 399\u2013406"},{"key":"1500_CR32","doi-asserted-by":"crossref","unstructured":"Zhang K, Gool LV, Timofte R (2020) Deep unfolding network for image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 3217\u20133226","DOI":"10.1109\/CVPR42600.2020.00328"},{"issue":"2","key":"1500_CR33","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/JSTSP.2020.3037516","volume":"15","author":"Q Ning","year":"2021","unstructured":"Ning Q, Dong W, Shi G, Li L, Li X (2021) Accurate and lightweight image super-resolution with model-guided deep unfolding network. IEEE J Sel Top Sig Process 15(2):240\u2013252","journal-title":"IEEE J Sel Top Sig Process"},{"key":"1500_CR34","doi-asserted-by":"crossref","unstructured":"Efrat N, Glasner D, Apartsin A, Nadler B, Levin A (2013) Accurate blur models vs. image priors in single image super-resolution. In: Proceedings of the IEEE international conference on computer vision (CVPR), pp 2832\u20132839","DOI":"10.1109\/ICCV.2013.352"},{"issue":"2","key":"1500_CR35","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1109\/TIP.2006.888334","volume":"16","author":"H Shen","year":"2007","unstructured":"Shen H, Zhang L, Huang B, Li P (2007) A map approach for joint motion estimation, segmentation, and super resolution. IEEE Trans Image Process 16(2):479\u2013490","journal-title":"IEEE Trans Image Process"},{"key":"1500_CR36","doi-asserted-by":"crossref","unstructured":"Bell JB (1978) Solutions of Ill-Posed Problems. JSTOR","DOI":"10.2307\/2006360"},{"issue":"1","key":"1500_CR37","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1117\/1.601623","volume":"37","author":"RC Hardie","year":"1998","unstructured":"Hardie RC, Barnard KJ, Bognar JG, Armstrong EE, Watson EA (1998) High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system. Opt Eng 37(1):247\u2013260","journal-title":"Opt Eng"},{"key":"1500_CR38","doi-asserted-by":"crossref","unstructured":"Brudfors M, Balbastre Y, Nachev P, Ashburner J (2018) MRI super-resolution using multi-channel total variation. In: 22nd Conference on medical image understanding and analysis (MIUA), pp 217\u2013228","DOI":"10.1007\/978-3-319-95921-4_21"},{"issue":"10","key":"1500_CR39","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.3390\/rs12101642","volume":"12","author":"P Cascarano","year":"2020","unstructured":"Cascarano P, Corsini F, Gandolfi S, Piccolomini EL, Mandanici E, Tavasci L, Zama F (2020) Super-resolution of thermal images using an automatic total variation based method. Remote Sens 12(10):1642","journal-title":"Remote Sens"},{"issue":"4","key":"1500_CR40","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.image.2014.01.002","volume":"29","author":"J Lu","year":"2014","unstructured":"Lu J, Zhang H, Sun Y (2014) Video super resolution based on non-local regularization and reliable motion estimation. Sig Process Image Commun 29(4):514\u2013529","journal-title":"Sig Process Image Commun"},{"key":"1500_CR41","doi-asserted-by":"crossref","unstructured":"Liu J, Tang J, Wu G (2020) Residual feature distillation network for lightweight image super-resolution. In: Proceedings of the European conference on computer vision workshops (ECCVW), Springer, pp 41\u201355","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"1500_CR42","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Proceeding of the 18th international conference on medical image computing and computer-assisted intervention (MICCAI), Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1500_CR43","unstructured":"Mao X, Shen C, Yang Y-B (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Adv Neural Inform Process Syst 29"},{"key":"1500_CR44","doi-asserted-by":"crossref","unstructured":"Hui Z, Gao X, Yang Y, Wang X (2019) Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th ACM international conference on multimedia, pp 2024\u20132032","DOI":"10.1145\/3343031.3351084"},{"key":"1500_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2021.103300","volume":"80","author":"L Wang","year":"2021","unstructured":"Wang L, Shen J, Tang E, Zheng S, Xu L (2021) Multi-scale attention network for image super-resolution. J Vis Commun Image Represent 80:103300","journal-title":"J Vis Commun Image Represent"},{"key":"1500_CR46","doi-asserted-by":"crossref","unstructured":"Niu B, Wen W, Ren W, Zhang X, Yang L, Wang S, Zhang K, Cao X, Shen H (2020) Single image super-resolution via a holistic attention network. In: Proceedings of the European conference on computer vision (ECCV), Springer, pp 191\u2013207","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"1500_CR47","doi-asserted-by":"publisher","first-page":"883","DOI":"10.5194\/isprs-archives-XLI-B3-883-2016","volume":"41","author":"L Liebel","year":"2016","unstructured":"Liebel L, K\u00f6rner M (2016) Single-image super resolution for multispectral remote sensing data using convolutional neural networks. Int Arch Photogramm Remote Sens Spat Inf Sci 41:883\u2013890","journal-title":"Int Arch Photogramm Remote Sens Spat Inf Sci"},{"key":"1500_CR48","doi-asserted-by":"crossref","unstructured":"Xu W, Guangluan X, Wang Y, Sun X, Lin D, Yirong W (2018) High quality remote sensing image super-resolution using deep memory connected network. In: IEEE International geoscience and remote sensing symposium (IGARSS), pp 8889\u20138892","DOI":"10.1109\/IGARSS.2018.8518855"},{"issue":"11","key":"1500_CR49","doi-asserted-by":"publisher","first-page":"9277","DOI":"10.1109\/TGRS.2019.2924818","volume":"57","author":"JM Haut","year":"2019","unstructured":"Haut JM, Fernandez-Beltran R, Paoletti ME, Plaza J, Plaza A (2019) Remote sensing image superresolution using deep residual channel attention. IEEE Trans Geosci Remote Sens 57(11):9277\u20139289","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1500_CR50","doi-asserted-by":"crossref","unstructured":"Mishra D, Hadar (2023) Accelerating neural style-transfer using contrastive learning for unsupervised satellite image super-resolution. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2023.3314283"},{"issue":"12","key":"1500_CR51","doi-asserted-by":"publisher","first-page":"8436","DOI":"10.1109\/TCSVT.2022.3194169","volume":"32","author":"D Cheng","year":"2022","unstructured":"Cheng D, Chen L, Lv C, Guo L, Kou Q (2022) light-guided and cross-fusion U-net for anti-illumination image super-resolution. IEEE Trans Circuits Syst Video Technol 32(12):8436\u20138449","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1500_CR52","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TCI.2020.2964202","volume":"6","author":"Y Li","year":"2020","unstructured":"Li Y, Tofighi M, Geng J, Monga V, Eldar YC (2020) Efficient and interpretable deep blind image deblurring via algorithm unrolling. IEEE Transactions on computational imaging 6:666\u2013681","journal-title":"IEEE Transactions on computational imaging"},{"key":"1500_CR53","doi-asserted-by":"publisher","first-page":"8443","DOI":"10.1109\/TIP.2020.3014729","volume":"29","author":"I Marivani","year":"2020","unstructured":"Marivani I, Tsiligianni E, Cornelis B, Deligiannis N (2020) Multimodal deep unfolding for guided image super-resolution. IEEE Trans Image Process 29:8443\u20138456","journal-title":"IEEE Trans Image Process"},{"key":"1500_CR54","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TCI.2022.3210329","volume":"8","author":"J Wang","year":"2022","unstructured":"Wang J, Shao Z, Huang X, Lu T, Zhang R (2022) A deep unfolding method for satellite super resolution. IEEE Transactions on computational imaging 8:933\u2013944","journal-title":"IEEE Transactions on computational imaging"},{"issue":"8","key":"1500_CR55","doi-asserted-by":"publisher","first-page":"3683","DOI":"10.1109\/TIP.2016.2567075","volume":"25","author":"N Zhao","year":"2016","unstructured":"Zhao N, Wei Q, Basarab A, Dobigeon N, Kouam\u00e9 D, Tourneret J-Y (2016) Fast single image super-resolution using a new analytical solution for $$\\ell 2$$-$$\\ell 2$$ problems. IEEE Trans Image Process 25(8):3683\u20133697","journal-title":"IEEE Trans Image Process"},{"key":"1500_CR56","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud PH, Riegler MA, Johansen D, De\u00a0Lange T, Halvorsen P, Johansen HD (2019) Resunet++: An advanced architecture for medical image segmentation. In: 2019 IEEE International symposium on multimedia (ISM), IEEE, pp 225\u20132255","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"1500_CR57","doi-asserted-by":"crossref","unstructured":"Li Y, Zhang Y, Timofte R, Van\u00a0Gool L, Tu Z, Du K, Wang H, Chen H, Li W, Wang X et al (2023) Ntire 2023 challenge on image denoising: Methods and results. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 1904\u20131920","DOI":"10.1109\/CVPRW59228.2023.00188"},{"issue":"1","key":"1500_CR58","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","volume":"3","author":"H Zhao","year":"2016","unstructured":"Zhao H, Gallo O, Frosio I, Kautz J (2016) Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging 3(1):47\u201357","journal-title":"IEEE Transactions on Computational Imaging"},{"key":"1500_CR59","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 270\u2013279","DOI":"10.1145\/1869790.1869829"},{"issue":"1","key":"1500_CR60","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/LGRS.2010.2055033","volume":"8","author":"D Dai","year":"2010","unstructured":"Dai D, Yang W (2010) Satellite image classification via two-layer sparse coding with biased image representation. IEEE Geosci Remote Sens Lett 8(1):173\u2013176","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"11","key":"1500_CR61","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","volume":"12","author":"Q Zou","year":"2015","unstructured":"Zou Q, Ni L, Zhang T, Wang Q (2015) Deep learning based feature selection for remote sensing scene classification. IEEE Geosci Remote Sens Lett 12(11):2321\u20132325","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1500_CR62","doi-asserted-by":"publisher","unstructured":"Team MS (2015) MODIS\/Terra Surface Reflectance Daily L2G Global 500m SIN Grid V006 (MOD09GA). NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD. https:\/\/doi.org\/10.5067\/MODIS\/MOD09GA.006. Accessed 04 Nov 2023","DOI":"10.5067\/MODIS\/MOD09GA.006"},{"key":"1500_CR63","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th IEEE international conference on computer vision (ICCV), vol 2, pp 416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"1500_CR64","doi-asserted-by":"crossref","unstructured":"Agustsson E, Timofte R (2017) Ntire 2017 challenge on single image super-resolution: Dataset and study. In: 2017 IEEE Conference on computer vision and pattern recognition workshops (CVPRW)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"1500_CR65","unstructured":"Kingma D, Ba J (2014) Adam: A method for stochastic optimization. Comput Sci"},{"key":"1500_CR66","doi-asserted-by":"crossref","unstructured":"Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang M-H (2022) Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5728\u20135739","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"1500_CR67","doi-asserted-by":"crossref","unstructured":"Shi F, Cheng J, Wang L, Yap P-T, Shen D (2013) Low-rank total variation for image super-resolution. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2013: 16th international conference, Nagoya, Japan, September 22\u201326, 2013, Proceedings, Part I 16, Springer, pp 155\u2013162","DOI":"10.1007\/978-3-642-40811-3_20"},{"key":"1500_CR68","doi-asserted-by":"crossref","unstructured":"Deng W, Yuan H, Deng L, Lu Z (2023) Reparameterized residual feature network for lightweight image super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1712\u20131721","DOI":"10.1109\/CVPRW59228.2023.00172"},{"issue":"12","key":"1500_CR69","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"A Mittal","year":"2012","unstructured":"Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695\u20134708","journal-title":"IEEE Trans Image Process"},{"key":"1500_CR70","doi-asserted-by":"crossref","unstructured":"Venkatanath N, Praneeth D, Bh MC, Channappayya SS, Medasani SS (2015) Blind image quality evaluation using perception based features. In: 2015 Twenty first national conference on communications (NCC), IEEE, pp 1\u20136","DOI":"10.1109\/NCC.2015.7084843"},{"key":"1500_CR71","doi-asserted-by":"crossref","unstructured":"Mittal A, Soundararajan R, Bovik AC (2012) Making a \u201ccompletely blind\u201d image quality analyzer. IEEE Signal Process Lett 20(3):209\u2013212","DOI":"10.1109\/LSP.2012.2227726"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01500-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01500-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01500-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:05:38Z","timestamp":1745654738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01500-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,30]]},"references-count":71,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1500"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01500-6","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,30]]},"assertion":[{"value":"24 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2024","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 data used in this study is openly available.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"128"}}