{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:17:31Z","timestamp":1775873851316,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11388-w","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T03:02:32Z","timestamp":1701658952000},"page":"11807-11821","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Image Super-Resolution Based on Gated Residual and Gated Convolution Networks"],"prefix":"10.1007","volume":"55","author":[{"given":"Xiaoang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yali","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Wenan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Shigang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"11388_CR1","unstructured":"Yu M, Wang H, Liu M et al (2021) Overview of research on image super-resolution reconstruction. Int Conf Inform Commun Softw Eng, pp 131\u2013135"},{"key":"11388_CR2","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.neucom.2019.03.106","volume":"398","author":"Z Wang","year":"2020","unstructured":"Wang Z, Jiang K, Yi P et al (2020) Ultra-dense GAN for satellite imagery super-resolution. Neurocomputing 398:328\u2013337","journal-title":"Neurocomputing"},{"key":"11388_CR3","doi-asserted-by":"publisher","unstructured":"Lyu Q, You C, Shan H et al (2018) Super-resolution MRI through deep learning. DOI:https:\/\/doi.org\/10.48550\/arXiv.1810.06776","DOI":"10.48550\/arXiv.1810.06776"},{"issue":"23","key":"11388_CR4","first-page":"165502","volume":"7","author":"S Park","year":"2020","unstructured":"Park S, Kang Y, Park J et al (2020) Self-controllable super-resolution deep learning framework for surveillance drones in security applications. Secur Saf 7(23):165502","journal-title":"Secur Saf"},{"issue":"8","key":"11388_CR5","doi-asserted-by":"publisher","first-page":"2226","DOI":"10.1109\/TIP.2006.877407","volume":"15","author":"L Zhang","year":"2006","unstructured":"Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226\u20132238","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"11388_CR6","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/TIP.2012.2208977","volume":"21","author":"K Zhang","year":"2012","unstructured":"Zhang K, Gao X, Tao D et al (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process 21(11):4544\u20134556","journal-title":"IEEE Trans Image Process"},{"key":"11388_CR7","doi-asserted-by":"crossref","unstructured":"Timofte R, Smet VD, Gool LV (2014) Anchored neighborhood regression for fast example-based super-resolution. In: IEEE international conference on computer vision, pp 1920\u20131927","DOI":"10.1109\/ICCV.2013.241"},{"key":"11388_CR8","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K et al (2014) Learning a deep convolutional network for image super-resolution. In: European conference on computer vision, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"11388_CR9","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: European conference on computer vision, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"11388_CR10","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"11388_CR11","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: IEEE conference on computer vision and pattern recognition, pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"11388_CR12","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11388_CR13","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE conference on computer vision and pattern recognition, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"key":"11388_CR14","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, pp 448\u2013456"},{"key":"11388_CR15","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H et al (2017) Enhanced deep residual networks for single image super-resolution. In: IEEE conference on computer vision and pattern recognition, pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"key":"11388_CR16","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y et al (2018) Residual dense network for image super-resolution. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"11388_CR17","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K et al (2018) Image super-resolution using very deep residual channel attention networks. In: European conference on computer vision, pp 294\u2013310","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"11388_CR18","doi-asserted-by":"crossref","unstructured":"Dai T, Cai J, Zhang Y et al (2019) Second-order attention network for single image super-resolution. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 11057\u201311066","DOI":"10.1109\/CVPR.2019.01132"},{"key":"11388_CR19","doi-asserted-by":"crossref","unstructured":"Liu J, Zhang W, Tang Y et al (2020) Residual feature aggregation network for image super-resolution. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 2356\u20132365","DOI":"10.1109\/CVPR42600.2020.00243"},{"issue":"7","key":"11388_CR20","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1364\/JOSA.54.000931","volume":"54","author":"JL Harris","year":"1964","unstructured":"Harris JL (1964) Diffraction and resolving power. J Opt Soc Am 54(7):931\u2013933","journal-title":"J Opt Soc Am"},{"key":"11388_CR21","first-page":"317","volume":"1","author":"RY Tsai","year":"1984","unstructured":"Tsai RY, Huang TS (1984) Multi-frame image restoration and registration. Adv Comput Vis Image Proc 1:317\u2013339","journal-title":"Adv Comput Vis Image Proc"},{"issue":"5","key":"11388_CR22","first-page":"1218","volume":"42","author":"H Gao","year":"2019","unstructured":"Gao H, Yuan H, Wang Z et al (2019) Pixel transposed convolutional networks. IEEE Trans Pattern Anal Mach Intell 42(5):1218\u20131227","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11388_CR23","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. Neural Inform Proc Syst 27:2672\u20132680","journal-title":"Neural Inform Proc Syst"},{"key":"11388_CR24","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"issue":"8","key":"11388_CR25","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2019","unstructured":"Hu J, Shen L, Sun G et al (2019) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11388_CR26","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X et al (2017) MemNet: a persistent memory network for image restoration. Comput Vis Pattern Recognit, pp 4549\u20134557","DOI":"10.1109\/ICCV.2017.486"},{"key":"11388_CR27","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F et al (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE conference on computer vision and pattern recognition, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"11388_CR28","doi-asserted-by":"crossref","unstructured":"Timofte R, Agustsson E, Gool LV et al (2017) NTIRE 2017 challenge on single image super-resolution: Methods and results. In: IEEE Conference on computer vision and pattern recognition workshops, pp 1110\u20131121","DOI":"10.1109\/CVPRW.2017.150"},{"key":"11388_CR29","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C et al (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: British machine vision conference, pp 77\u201386","DOI":"10.5244\/C.26.135"},{"key":"11388_CR30","doi-asserted-by":"crossref","unstructured":"Zeyde R, Protter M, Elad M et al (2010) On single image scale-up using sparse-representations. Curves and Surfaces, pp 711\u2013730","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"11388_CR31","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D et al (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: IEEE international conference on computer vision, pp 416\u2013425","DOI":"10.1109\/ICCV.2001.937655"},{"key":"11388_CR32","doi-asserted-by":"crossref","unstructured":"Huang J, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: IEEE conference on computer vision and pattern recognition, pp 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"20","key":"11388_CR33","doi-asserted-by":"publisher","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","volume":"76","author":"Y Matsui","year":"2017","unstructured":"Matsui Y, Ito K, Aramaki Y et al (2017) Sketch-based manga retrieval using manga109 dataset. Multimedia Tools Appl 76(20):21811\u201321838","journal-title":"Multimedia Tools Appl"},{"key":"11388_CR34","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo M, Zhang L et al (2018) Learning a single convolutional super-resolution network for multiple degradations. In: IEEE\/CVF conference on computer vision and pattern recognition, pp 3262\u20133271","DOI":"10.1109\/CVPR.2018.00344"},{"key":"11388_CR35","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. In: International conference on learning representations"},{"key":"11388_CR36","unstructured":"Paszke A, Gross S, Chintala S et al (2017) Automatic differentiation in pytorch. NIPS 2017 Autodiff Workshop"},{"key":"11388_CR37","doi-asserted-by":"crossref","unstructured":"Lai W, Huang J, Ahuja N et al (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: IEEE conference on computer vision and pattern recognition, pp 5835\u20135843","DOI":"10.1109\/CVPR.2017.618"},{"key":"11388_CR38","doi-asserted-by":"publisher","unstructured":"You C, Yang L, Zhang Y et al (2019) Low-dose CT via deep CNN with skip connection and network in network. DOI:https:\/\/doi.org\/10.48550\/arXiv.1811.10564","DOI":"10.48550\/arXiv.1811.10564"},{"key":"11388_CR39","doi-asserted-by":"crossref","unstructured":"You C, Yang Q, Shan H et al (2018) Structurally-sensitive multi-scale deep neural network for low-dose CT denoising. IEEE Access, pp 41839\u201341855","DOI":"10.1109\/ACCESS.2018.2858196"},{"key":"11388_CR40","doi-asserted-by":"publisher","unstructured":"You C, Zhao R, Liu F et al (2022) Class-aware generative adversarial transformers for medical image segmentation. DOI:https:\/\/doi.org\/10.48550\/arXiv.2201.10737","DOI":"10.48550\/arXiv.2201.10737"},{"key":"11388_CR41","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1109\/TMI.2019.2922960","volume":"39","author":"C You","year":"2019","unstructured":"You C, Li G, Zhang X et al (2019) CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE). IEEE Trans Med Imagin 39:188\u2013203","journal-title":"IEEE Trans Med Imagin"},{"key":"11388_CR42","doi-asserted-by":"crossref","unstructured":"Lyu Q, You C, Shan H et al (2019) Super-resolution MRI and CT through GAN-CIRCLE. Developments in X-Ray Tomography XII","DOI":"10.1117\/12.2530592"},{"key":"11388_CR43","doi-asserted-by":"crossref","unstructured":"You C, Han L, Feng A, et al (2022) Megan: memory enhanced graph attention network for space-time video super-resolution. IEEE\/CVF winter conference on applications of computer vision, pp 1401\u20131411","DOI":"10.1109\/WACV51458.2022.00400"},{"key":"11388_CR44","doi-asserted-by":"crossref","unstructured":"Chen S, Bi X, Zhang L (2023) Fused pyramid attention network for single image super-resolution. IET Image Processing, pp 1681\u20131693","DOI":"10.1049\/ipr2.12746"},{"key":"11388_CR45","first-page":"13067","volume-title":"Single image super-resolution via a ternary attention network","author":"L Yang","year":"2023","unstructured":"Yang L, Tang J, Niu B et al (2023) Single image super-resolution via a ternary attention network. Springer, Berlin, pp 13067\u201313081"},{"key":"11388_CR46","doi-asserted-by":"crossref","unstructured":"Chen J, Wang W, Xing F et al (2023) Multi-feature fusion attention network for single image super-resolution. IET Image Processing, pp 1389\u20131402","DOI":"10.1049\/ipr2.12721"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11388-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11388-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11388-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T04:29:16Z","timestamp":1703651356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11388-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":46,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11388"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11388-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2714014\/v1","asserted-by":"object"}]},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12]]},"assertion":[{"value":"1 August 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2023","order":2,"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"}}]}}