{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:17:09Z","timestamp":1776086229741,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T00:00:00Z","timestamp":1750896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Wenzhou Major Science and Technology Innovation Project","award":["ZG2023011"],"award-info":[{"award-number":["ZG2023011"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ25F010007"],"award-info":[{"award-number":["LZ25F010007"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s11760-025-04185-6","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T10:35:00Z","timestamp":1750934100000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Super-Resolution Image Reconstruction based on Random-coupled Neural Network and EDSR"],"prefix":"10.1007","volume":"19","author":[{"given":"Xue","family":"Zuo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingzhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongfen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaimin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruili","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"issue":"10","key":"4185_CR1","doi-asserted-by":"publisher","first-page":"30467","DOI":"10.1007\/s11042-023-16197-w","volume":"83","author":"W El-Shafai","year":"2024","unstructured":"El-Shafai, W., Ali, A.M., El-Nabi, S.A., El-Rabaie, E.S.M., Abd El-Samie, F.E.: Single image super-resolution approaches in medical images based on deep learning: a survey. In Multimedia Tools and Applications 83(10), 30467\u201330503 (2024)","journal-title":"In Multimedia Tools and Applications"},{"key":"4185_CR2","doi-asserted-by":"publisher","first-page":"21815","DOI":"10.1007\/s11042-020-08980-w","volume":"79","author":"Y Gu","year":"2020","unstructured":"Gu, Y., Zeng, Z., Chen, H., Wei, J., Zhang, Y., Chen, B., et al.: MedSRGAN: medical images super-resolution using generative adversarial networks. In Multimedia Tools and Applications 79, 21815\u201321840 (2020)","journal-title":"In Multimedia Tools and Applications"},{"issue":"21","key":"4185_CR3","doi-asserted-by":"publisher","first-page":"5423","DOI":"10.3390\/rs14215423","volume":"14","author":"X Wang","year":"2022","unstructured":"Wang, X., Yi, J., Guo, J., Song, Y., Lyu, J., Xu, J., et al.: A review of image super-resolution approaches based on deep learning and applications in remote sensing. In Remote Sensing 14(21), 5423 (2022)","journal-title":"In Remote Sensing"},{"issue":"22","key":"4185_CR4","doi-asserted-by":"publisher","first-page":"3812","DOI":"10.3390\/rs12223812","volume":"12","author":"F Dorr","year":"2020","unstructured":"Dorr, F.: Satellite image multi-frame super resolution using 3D wide-activation neural networks. In Remote Sensing 12(22), 3812 (2020)","journal-title":"In Remote Sensing"},{"key":"4185_CR5","doi-asserted-by":"publisher","first-page":"23815","DOI":"10.1007\/s11042-018-5915-7","volume":"78","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali, P., Zareapoor, M., Jain, D.K., Jain, V.K., Yang, J.: Deep convolution network for surveillance records super-resolution. In Multimedia Tools and Applications 78, 23815\u201323829 (2019)","journal-title":"In Multimedia Tools and Applications"},{"issue":"7","key":"4185_CR6","doi-asserted-by":"publisher","first-page":"867","DOI":"10.3390\/electronics10070867","volume":"10","author":"YK Ooi","year":"2021","unstructured":"Ooi, Y.K., Ibrahim, H.: Deep learning algorithms for single image super-resolution: a systematic review. In Electronics 10(7), 867 (2021)","journal-title":"In Electronics"},{"key":"4185_CR7","doi-asserted-by":"crossref","unstructured":"Patil, V. H., Bormane, D.S.: Interpolation for super resolution imaging. In Proceedings of Innovations and Advanced Techniques in Computer and Information Sciences and Engineering, 483\u2013489, 2007","DOI":"10.1007\/978-1-4020-6268-1_85"},{"key":"4185_CR8","doi-asserted-by":"crossref","unstructured":"Allebach, J., Wong, P.W.: Edge-directed interpolation. In Proceedings of 3rd IEEE International Conference on Image Processing, 707\u2013710, 1996","DOI":"10.1109\/ICIP.1996.560768"},{"key":"4185_CR9","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J. K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1646\u20131654, (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"4185_CR10","doi-asserted-by":"crossref","unstructured":"Park, S. W., Jung, S. H., Sim, C.B.: NeXtSRGAN: enhancing super-resolution GAN with ConvNeXt discriminator for superior realism. In The Visual Computer, :1\u201327, (2025)","DOI":"10.1007\/s00371-024-03797-2"},{"key":"4185_CR11","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4681\u20134690, (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"4185_CR12","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 136\u2013144, (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"14","key":"4185_CR13","doi-asserted-by":"publisher","first-page":"2137","DOI":"10.3390\/electronics11142137","volume":"11","author":"H Park","year":"2022","unstructured":"Park, H.: Semantic Super-Resolution of Text Images via Self-Distillation. In Electronics 11(14), 2137 (2022)","journal-title":"In Electronics"},{"key":"4185_CR14","doi-asserted-by":"crossref","unstructured":"Maruyama, D., Kanai, K., Katto, J.: Performance evaluations of channel estimation using deep-learning based super-resolution. In Proceedings of 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), 1\u20136, (2021)","DOI":"10.1109\/CCNC49032.2021.9369521"},{"issue":"9","key":"4185_CR15","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.1109\/TNS.2023.3305304","volume":"70","author":"R Liu","year":"2023","unstructured":"Liu, R., Sun, Y., Liu, X., Cong, P.: Enhanced Data Augmentation for Denoising and Super-Resolution Reconstruction of Radiation Images. In IEEE Transactions on Nuclear Science 70(9), 2183\u20132190 (2023)","journal-title":"In IEEE Transactions on Nuclear Science"},{"issue":"1","key":"4185_CR16","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1038\/s41467-023-35973-8","volume":"14","author":"YD Wang","year":"2023","unstructured":"Wang, Y.D., Meyer, Q., Tang, K., McClure, J.E., White, R.T., Kelly, S.T., et al.: Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning. In Nature Communications 14(1), 745 (2023)","journal-title":"In Nature Communications"},{"issue":"1","key":"4185_CR17","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s11277-021-08460-w","volume":"120","author":"F Deeba","year":"2021","unstructured":"Deeba, F., Zhou, Y., Dharejo, F.A., Du, Y., Wang, X., Kun, S.: Multi-scale single image super-resolution with remote-sensing application using transferred wide residual network. In Wireless Personal Communications 120(1), 323\u2013342 (2021)","journal-title":"In Wireless Personal Communications"},{"issue":"9","key":"4185_CR18","doi-asserted-by":"publisher","first-page":"2446","DOI":"10.1049\/ipr2.12499","volume":"16","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Cao, L., Wang, T., Fu, W., Shen, W.: NHNet: A non?local hierarchical network for image denoising. In IET Image Processing 16(9), 2446\u20132456 (2022)","journal-title":"In IET Image Processing"},{"key":"4185_CR19","doi-asserted-by":"crossref","unstructured":"Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7268\u20137277, (2018)","DOI":"10.1109\/CVPR.2018.00759"},{"key":"4185_CR20","doi-asserted-by":"publisher","first-page":"1226024","DOI":"10.3389\/fmars.2023.1226024","volume":"10","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Zhao, L., Zhong, T., Jia, Y., Cui, Y.: Generative adversarial networks with multi-scale and attention mechanisms for underwater image enhancement. In Frontiers in Marine Science 10, 1226024 (2023)","journal-title":"In Frontiers in Marine Science"},{"issue":"2","key":"4185_CR21","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1007\/s11063-022-10934-2","volume":"55","author":"X Jia","year":"2023","unstructured":"Jia, X., Peng, Y., Ge, B., Li, J., Liu, S., Wang, W.: A multi-scale dilated residual convolution network for image denoising. In Neural Processing Letters 55(2), 1231\u20131246 (2023)","journal-title":"In Neural Processing Letters"},{"issue":"11","key":"4185_CR22","doi-asserted-by":"publisher","first-page":"5953","DOI":"10.1007\/s00371-022-02705-w","volume":"39","author":"L Qian","year":"2023","unstructured":"Qian, L., Huang, H., Xia, X., Li, Y., Zhou, X.: Automatic segmentation method using FCN with multi-scale dilated convolution for medical ultrasound image. In The Visual Computer 39(11), 5953\u20135969 (2023)","journal-title":"In The Visual Computer"},{"key":"4185_CR23","doi-asserted-by":"crossref","unstructured":"Hu, H., Yu, C., Zhou, Q., Guan, Q., Chen, Q.: SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution. In Proceedings of Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 460\u2013472, (2023)","DOI":"10.1007\/978-981-99-8543-2_37"},{"issue":"1","key":"4185_CR24","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1038\/s41467-023-35973-8","volume":"14","author":"YD Wang","year":"2023","unstructured":"Wang, Y.D., Meyer, Q., Tang, K., McClure, J.E., White, R.T., Kelly, S.T., et al.: Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning. In Nature Communications 14(1), 745 (2023)","journal-title":"In Nature Communications"},{"key":"4185_CR25","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In Proceedings of the European Conference on Computer Vision (ECCV), 517\u2013532, (2018)","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"4185_CR26","unstructured":"Yang, X., Zhu, Y., Guo, Y., Zhou, D.: An image super-resolution network based on multi-scale convolution fusion. In The Visual Computer, :1\u201311, (2022)"},{"key":"4185_CR27","doi-asserted-by":"crossref","unstructured":"Liu, H. et al.: Random coupled neural network. Electronics 13(21), 4297 (2024)","DOI":"10.3390\/electronics13214297"},{"issue":"20","key":"4185_CR28","doi-asserted-by":"publisher","first-page":"3264","DOI":"10.3390\/electronics11203264","volume":"11","author":"H Liu","year":"2022","unstructured":"Liu, H., Liu, M., Li, D., Zheng, W., Yin, L., Wang, R.: Recent advances in pulse-coupled neural networks with applications in image processing. In Electronics 11(20), 3264 (2022)","journal-title":"In Electronics"},{"key":"4185_CR29","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, P., Liu, M., et al.: Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU. In IEEE Transactions on Nuclear Science, (2024)","DOI":"10.1109\/TNS.2024.3444888"},{"issue":"8","key":"4185_CR30","doi-asserted-by":"publisher","first-page":"3965","DOI":"10.1016\/j.eswa.2013.12.027","volume":"41","author":"MM Subashini","year":"2014","unstructured":"Subashini, M.M., Sahoo, S.K.: Pulse coupled neural networks and its applications. In Expert Systems with Applications 41(8), 3965\u20133974 (2014)","journal-title":"In Expert Systems with Applications"},{"key":"4185_CR31","first-page":"573","volume":"24","author":"K Zhan","year":"2017","unstructured":"Zhan, K., Shi, J., Wang, H., Xie, Y., Li, Q.: Computational mechanisms of pulse-coupled neural networks: a comprehensive review. In Archives of Computational Methods in Engineering 24, 573\u2013588 (2017)","journal-title":"In Archives of Computational Methods in Engineering"},{"key":"4185_CR32","first-page":"2017","volume":"126\u2013135","author":"E Agustsson","year":"2017","unstructured":"Agustsson, E., Timofte, R. Ntire.: challenge on single image super-resolution: Dataset and study. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 126\u2013135, 2017 (2017)","journal-title":"In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"4185_CR33","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In Proceedings of 2010 20th International Conference on Pattern Recognition, 2366\u20132369, (2010)","DOI":"10.1109\/ICPR.2010.579"},{"issue":"8","key":"4185_CR34","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: A feature similarity index for image quality assessment. In IEEE Transactions on Image Processing 20(8), 2378\u20132386 (2011)","journal-title":"In IEEE Transactions on Image Processing"},{"key":"4185_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A. A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 586\u2013595, (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"4185_CR36","unstructured":"Kingma, D. P., Ba, J.: Adam: A method for stochastic optimization. In arXiv preprint arXiv:1412.6980, (2014)"},{"key":"4185_CR37","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2623\u20132631, (2019)","DOI":"10.1145\/3292500.3330701"},{"key":"4185_CR38","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, 1440\u20131448, (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"4185_CR39","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J. K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1646\u20131654, (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"4185_CR40","doi-asserted-by":"publisher","first-page":"1927","DOI":"10.1109\/TIP.2023.3256763","volume":"32","author":"Y Song","year":"2023","unstructured":"Song, Y., He, Z., Qian, H., Du, X.: Vision transformers for single image dehazing. In IEEE Transactions on Image Processing 32, 1927\u20131941 (2023)","journal-title":"In IEEE Transactions on Image Processing"},{"key":"4185_CR41","doi-asserted-by":"crossref","unstructured":"Pan, X., Ge, C., Lu, R., Song, S., Chen, G., Huang, Z., Huang, G.: On the integration of self-attention and convolution. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 815\u2013825, (2022)","DOI":"10.1109\/CVPR52688.2022.00089"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04185-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04185-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04185-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T22:43:56Z","timestamp":1757198636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04185-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,26]]},"references-count":41,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["4185"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04185-6","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,26]]},"assertion":[{"value":"20 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2025","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 declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest\/Competing interests"}},{"value":"Not applicable. This study did not involve human participants or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to"}},{"value":"Not applicable. This study does not contain any personal data or images.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The source code of this study is available at .","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials"}}],"article-number":"803"}}