{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T17:16:25Z","timestamp":1751476585141,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T00:00:00Z","timestamp":1701216000000},"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":"publisher","award":["62276266"],"award-info":[{"award-number":["62276266"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s13042-023-02030-1","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T08:02:45Z","timestamp":1701244965000},"page":"2309-2321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Single image super-resolution via global aware external attention and multi-scale residual channel attention network"],"prefix":"10.1007","volume":"15","author":[{"given":"Mingming","family":"Liu","sequence":"first","affiliation":[]},{"given":"Sui","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2365-6606","authenticated-orcid":false,"given":"Bing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuxin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,29]]},"reference":[{"key":"2030_CR1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/978-3-319-41778-3_18","volume-title":"International conference on articulated motion and deformable objects","author":"P Rasti","year":"2016","unstructured":"Rasti P, Uiboupin T, Escalera S et al (2016) Convolutional neural network super resolution for face recognition in surveillance monitoring. In: Perales FJ, Kittler J (eds) International conference on articulated motion and deformable objects. Springer International Publishing, Cham, pp 175\u2013184"},{"key":"2030_CR2","first-page":"246","volume-title":"Medical image computing and computer-assisted intervention","author":"O Oktay","year":"2016","unstructured":"Oktay O, Bai W, Lee M et al (2016) Multi-input cardiac image super-resolution using convolutional neural networks. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical image computing and computer-assisted intervention. Springer International Publishing, Cham, pp 246\u2013254"},{"key":"2030_CR3","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1109\/LGRS.2017.2766204","volume":"14","author":"Y Luo","year":"2017","unstructured":"Luo Y, Zhou L, Shu W et al (2017) Video satellite imagery super resolution via convolutional neural networks. IEEE Geosci Remote Sens Lett 14:2398\u20132402","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"2030_CR4","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"RG Keys","year":"2003","unstructured":"Keys RG (2003) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Proces 29:1153\u20131160","journal-title":"IEEE Trans Acoust Speech Signal Proces"},{"key":"2030_CR5","doi-asserted-by":"publisher","first-page":"3085","DOI":"10.1109\/TIP.2014.2325774","volume":"23","author":"Y Romano","year":"2014","unstructured":"Romano Y, Protter, et al (2014) Single image interpolation via adaptive nonlocal sparsity-based modeling. IEEE Trans Image Process 23:3085\u20133098","journal-title":"IEEE Trans Image Process"},{"key":"2030_CR6","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1109\/TIP.2018.2874284","volume":"28","author":"M Zhang","year":"2018","unstructured":"Zhang M, Desrosiers C (2018) High-quality image restoration using low-rank patch regularization and global structure sparsity. IEEE Trans Image Process 28:868\u2013879","journal-title":"IEEE Trans Image Process"},{"key":"2030_CR7","doi-asserted-by":"publisher","first-page":"3778","DOI":"10.1109\/TIP.2019.2902794","volume":"28","author":"C Ren","year":"2019","unstructured":"Ren C, He X, Pu Y et al (2019) Enhanced non-local total variation model and multi-directional feature prediction prior for single image super resolution. IEEE Trans Image Process 28:3778\u20133793","journal-title":"IEEE Trans Image Process"},{"key":"2030_CR8","doi-asserted-by":"publisher","DOI":"10.1049\/ell2.12689","author":"JH Kim","year":"2018","unstructured":"Kim JH, Lee JS (2018) Deep residual network with enhanced upscaling module for super-resolution. IEEE\/CVF Conf Comput Vis Patt Recogn Workshops. https:\/\/doi.org\/10.1049\/ell2.12689","journal-title":"IEEE\/CVF Conf Comput Vis Patt Recogn Workshops"},{"key":"2030_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182","author":"J Kim","year":"2016","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. IEEE Conf Comput Vis Patt Recogn. https:\/\/doi.org\/10.1109\/CVPR.2016.182","journal-title":"IEEE Conf Comput Vis Patt Recogn"},{"key":"2030_CR10","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong C, Loy CC, He K et al (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38:295\u2013307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2030_CR11","first-page":"275","volume":"34","author":"H Chang","year":"2004","unstructured":"Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. IEEE Comput Soc Conf Comput Vis Patt Recogn 34:275\u2013282","journal-title":"IEEE Comput Soc Conf Comput Vis Patt Recogn"},{"key":"2030_CR12","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.neucom.2019.10.076","volume":"379","author":"J Qin","year":"2020","unstructured":"Qin J, Huang Y, Wen W (2020) Multi-scale feature fusion residual network for single image super-resolution. Neurocomputing 379:334\u2013342","journal-title":"Neurocomputing"},{"key":"2030_CR13","first-page":"527","volume-title":"European conference on computer vision","author":"J Li","year":"2018","unstructured":"Li J, Fang F, Mei K et al (2018) Multi-scale residual network for image super-resolution. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) European conference on computer vision. Springer International Publishing, Cham, pp 527\u2013542"},{"key":"2030_CR14","first-page":"294","volume-title":"European conference on computer vision","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Li K, Li K et al (2018) Image super-resolution using very deep residual channel attention networks. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) European conference on computer vision. Springer International Publishing, Cham, pp 294\u2013310"},{"key":"2030_CR15","first-page":"2472","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Tian Y, Kong Y et al (2018) Residual dense network for image super-resolution. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) IEEE\/CVF Conference on Computer Vision and Pattern Recognition. Springer International Publishing, Cham, pp 2472\u20132481"},{"key":"2030_CR16","unstructured":"Ying T, Jian Y, Liu X (2017) \u201cImage Super-Resolution via Deep Recursive Residual Network,\u201d in IEEE Conference on Computer Vision & Pattern Recognition., , pp. 2790\u20132798"},{"key":"2030_CR17","doi-asserted-by":"crossref","unstructured":"Kim J, Lee J K, Lee K M (2016) \u201cDeeply-Recursive Convolutional Network for Image Super-Resolution,\u201d in IEEE Conference on Computer Vision and Pattern Recognition., , pp.1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"2030_CR18","first-page":"391","volume-title":"European conference on computer vision","author":"D Chao","year":"2016","unstructured":"Chao D, Chen CL, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: Leibe B, Matas J, Sebe N, Welling M (eds) European conference on computer vision. Springer International Publishing, Cham, pp 391\u2013407"},{"key":"2030_CR19","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al (2016) \u201cDeep Residual Learning for Image Recognition,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, pp.770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2030_CR20","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, et al (2017) \u201cEnhanced Deep Residual Networks for Single Image Super-Resolution,\u201d in IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE, pp.1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2030_CR21","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Laurens V, et al (2016) \u201cDensely Connected Convolutional Networks,\u201d in IEEE Conference on Computer Vision and Pattern Recognition., , pp. 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"2030_CR22","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X, et al (2017) \u201cMemNet: A Persistent Memory Network for Image Restoration,\u201d in IEEE International Conference on Computer Vision., , pp. 4549\u20134557","DOI":"10.1109\/ICCV.2017.486"},{"key":"2030_CR23","doi-asserted-by":"crossref","unstructured":"Tong T, Li G, Liu X, et al (2017) \u201cImage Super-Resolution Using Dense Skip Connections,\u201d in IEEE International Conference on Computer Vision., , pp. 4809\u20134817","DOI":"10.1109\/ICCV.2017.514"},{"key":"2030_CR24","unstructured":"Guo, M.H., et al (2021) \u201cBeyond Self-attention: External Attention using Two Linear Layers for Visual Tasks,\u201d CoRR, vol.abs\/2105.02358"},{"key":"2030_CR25","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, F Husz\u00e1r, et al (2016)\u201cReal-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network,\u201d in IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"2030_CR26","doi-asserted-by":"crossref","unstructured":"Lai W S, Huang J B, Ahuja N, et al (2017) \u201cDeep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution,\u201d in IEEE Conference on Computer Vision & Pattern Recognition., , pp.5835\u20135843","DOI":"10.1109\/CVPR.2017.618"},{"key":"2030_CR27","unstructured":"Xiao M, Chuhua S, Yubin Y (2016) \u201cImage Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections,\u201d CoRR, vol.abs\/1606.08921"},{"key":"2030_CR28","unstructured":"Szegedy C, Liu W, Jia Y, et al (2014) \u201cGoing Deeper with Convolutions,\u201d in IEEE Computer Society., 2014, pp. 1\u20139"},{"key":"2030_CR29","unstructured":"Liu Y, Zhang X, Wang S, et al (2020) \u201cProgressive Multi-Scale Residual Network for Single Image Super-Resolution,\u201d CoRR, vol.abs\/2007.09552"},{"key":"2030_CR30","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/s10489-020-01869-z","volume":"51","author":"C Xiong","year":"2020","unstructured":"Xiong C, Shi X, Gao Z et al (2020) Attention augmented multi-scale network for single image super-resolution. Appl Intell 51:935\u2013951","journal-title":"Appl Intell"},{"key":"2030_CR31","unstructured":"Jie H, Li S, Gang S, et al (2017) \u201cSqueeze-and-Excitation Networks,\u201d in IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 7132\u20137141"},{"key":"2030_CR32","doi-asserted-by":"crossref","unstructured":"Dai T, Cai J, Zhang Y, et al (2019) \u201cSecond-order Attention Network for Single Image Super-Resolution,\u201d in IEEE Conference on Computer Vision and Pattern Recognition., pp. 11065\u201311074","DOI":"10.1109\/CVPR.2019.01132"},{"key":"2030_CR33","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, et al (2017) \u201cNon-local Neural Networks,\u201d CoRR, vol.abs\/1711.07971","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2030_CR34","unstructured":"Zhang Y, K Li, K Li, et al (2019) \u201cResidual non-local attention networks for image restoration,\u201d CoRR, vol.abs\/1903.10082"},{"key":"2030_CR35","doi-asserted-by":"crossref","unstructured":"Mei Y, Fan Y, Zhou Y, et al (2020) \u201cImage super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining,\u201d In IEEE\/CVF conference on computer vision and pattern recognition., , pp. 5689\u20135698","DOI":"10.1109\/CVPR42600.2020.00573"},{"key":"2030_CR36","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10489-020-01723-2","volume":"51","author":"Z Liu","year":"2021","unstructured":"Liu Z, Huang J, Zhu C et al (2021) Residual attention network using multi-channel dense connections for image super-resolution. Appl Intell 51:85\u201399","journal-title":"Appl Intell"},{"key":"2030_CR37","doi-asserted-by":"crossref","unstructured":"Hu X, Mu H, Zhang X, et al (2020) \u201cMeta-SR: A magnification-arbitrary network for super-resolution,\u201d In: IEEE conference on computer vision and pattern recognition., 2020, pp. 1575\u20131584","DOI":"10.1109\/CVPR.2019.00167"},{"key":"2030_CR38","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2018) \u201cDeep back-projection networks for super-resolution,\u201d arXiv. arXiv, , pp. 1664\u20131673","DOI":"10.1109\/CVPR.2018.00179"},{"key":"2030_CR39","doi-asserted-by":"crossref","unstructured":"Sajjadi M, Scholkopf B, Hirsch M (2017) \u201cEnhanceNet: single image super-resolution through automated texture synthesis,\u201d In: IEEE International Conference on Computer Vision., , pp. 4501\u20134510","DOI":"10.1109\/ICCV.2017.481"},{"key":"2030_CR40","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, F Huszar, et al (2016) \u201cPhoto-realistic single image super-resolution using a generative adversarial network,\u201d In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"2030_CR41","doi-asserted-by":"crossref","unstructured":"Wang Q, et al (2020) \u201cECA-Net: efficient channel attention for deep convolutional neural networks,\u201d In: IEEE conference on computer vision and pattern recognition., 2020, pp. 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"2030_CR42","doi-asserted-by":"crossref","unstructured":"Agustsson E, Timofte R (2017) \u201cNTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study,\u201d In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.1122\u20131131","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2030_CR43","unstructured":"Kingma D, Ba J (2015) \u201cAdam: a method for stochastic optimization,\u201d in international conference on learning representations"},{"key":"2030_CR44","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C, et al (2012) \u201cNeighbor embedding based single-image super-resolution using Semi-Nonnegative Matrix Factorization,\u201d in IEEE International Conference on Acoustics, pp.1289\u20131292","DOI":"10.1109\/ICASSP.2012.6288125"},{"key":"2030_CR45","first-page":"711","volume-title":"International conference on curves and surfaces","author":"R Zeyde","year":"2010","unstructured":"Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: Boissonnat J-D, Chenin P, Cohen A, Gout C, Lyche T, Mazure M-L, Schumaker L (eds) International conference on curves and surfaces. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 711\u2013730"},{"key":"2030_CR46","unstructured":"Martin D, Fowlkes C, Tal D, et al (2002) \u201cA database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,\u201d In: IEEE International Conference on Computer Vision, pp. 416\u2013425"},{"key":"2030_CR47","doi-asserted-by":"crossref","unstructured":"Huang J B, Singh A, Ahuja N (2015) \u201cSingle image super-resolution from transformed self-exemplars,\u201d In: IEEE Conference on Computer Vision and Pattern Recognition., pp. 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"2030_CR48","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. Multimed Tools Appl 76:21811\u201321838","journal-title":"Multimed Tools Appl"},{"key":"2030_CR49","doi-asserted-by":"publisher","first-page":"4656","DOI":"10.1109\/TIP.2020.2973769","volume":"29","author":"F Fang","year":"2020","unstructured":"Fang F, Li J, Zeng T (2020) Soft-edge assisted network for single image super-resolution. IEEE Trans Image Process 29:4656\u20134668","journal-title":"IEEE Trans Image Process"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02030-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-02030-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-02030-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T05:27:39Z","timestamp":1716442059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-02030-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,29]]},"references-count":49,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["2030"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-02030-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2023,11,29]]},"assertion":[{"value":"24 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}