{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:26Z","timestamp":1740122786588,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science Foundation of Sichuan Science and Technology Department","award":["2021YFH0119"],"award-info":[{"award-number":["2021YFH0119"]}]},{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"crossref","award":["2020SCUNG205"],"award-info":[{"award-number":["2020SCUNG205"]}],"id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11063-022-11007-0","type":"journal-article","created":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T17:02:29Z","timestamp":1661014949000},"page":"3225-3243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight Parallel Feedback Network for Image Super-Resolution"],"prefix":"10.1007","volume":"55","author":[{"given":"Beibei","family":"Wang","sequence":"first","affiliation":[]},{"given":"Changjun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Binyu","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Xiaomin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"key":"11007_CR1","doi-asserted-by":"crossref","unstructured":"Chao\u00a0Dong KH, Change\u00a0Loy Chen, Tang X (2014) Learning a deep convolutional network for image super-resolution In: Computer Vision \u2013 ECCV 2014, Vol 8692, 2014, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"11007_CR2","doi-asserted-by":"crossref","unstructured":"Chao\u00a0Dong CCL, Tang X (2016) Accelerating the super-resolution convolutional neural network In: Computer Vision \u2013 ECCV 2016, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"11007_CR3","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"11007_CR4","doi-asserted-by":"crossref","unstructured":"Kim J, Lee J, Lee K (2016) Accurate image super-resolution using very deep convolutional networks In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"11007_CR5","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"11007_CR6","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11007_CR7","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Husz\u00e1r F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"11007_CR8","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2790\u20132798","DOI":"10.1109\/CVPR.2017.298"},{"key":"11007_CR9","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"key":"11007_CR10","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X, Xu C (2017) Memnet: A persistent memory network for image restoration In: 2017 IEEE International Conference on Computer Vision (ICCV), pp 4549\u20134557","DOI":"10.1109\/ICCV.2017.486"},{"key":"11007_CR11","doi-asserted-by":"crossref","unstructured":"Lai W-S, Huang J-B, Ahuja N, Yang M-H (2017) Deep laplacian pyramid networks for fast and accurate super-resolution In: IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2017.618"},{"key":"11007_CR12","unstructured":"H.\u00a0L. Z. L. W. W. A. P. G. J. X.\u00a0Y. Shipeng\u00a0Fu, Lu\u00a0Lu (2019) A real-time super-resolution method based on convolutional neural networks In: Circuits,Systems,andSignalProcessing"},{"key":"11007_CR13","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van\u00a0der Maaten L, Weinberger K (2017) Densely connected convolutional networks","DOI":"10.1109\/CVPR.2017.243"},{"key":"11007_CR14","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.neuron.2007.05.019","volume":"54","author":"CD Gilbert","year":"2007","unstructured":"Gilbert CD, Sigman M (2007) Brain states: Top-down influences in sensory processing. Neuron 54:677\u2013696","journal-title":"Neuron"},{"key":"11007_CR15","unstructured":"Stollenga M, Masci J, Gomez F, Schmidhuber J (2014) Deep networks with internal selective attention through feedback connections, Vol\u00a04"},{"key":"11007_CR16","doi-asserted-by":"crossref","unstructured":"Zamir AR, Wu T-L, Sun L, Shen W, Shi BE, Malik J, Savarese S (2016) Feedback networks In: Computer Science - Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2017.196"},{"key":"11007_CR17","doi-asserted-by":"crossref","unstructured":"Carreira J, Agrawal P, Fragkiadaki K, Malik J (2016) Human pose estimation with iterative error feedback In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp 4733\u20134742","DOI":"10.1109\/CVPR.2016.512"},{"key":"11007_CR18","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1664\u20131673","DOI":"10.1109\/CVPR.2018.00179"},{"key":"11007_CR19","doi-asserted-by":"crossref","unstructured":"Li Z, Yang J, Liu Z, Yang X, Jeon G, Wu W (2019) Feedback network for image super-resolution In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2019.00399"},{"key":"11007_CR20","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition In: Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"11007_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu Wei, Jia Yangqing, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11007_CR22","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":"11007_CR23","doi-asserted-by":"crossref","unstructured":"Xie Y, Zhang Y, Qu Y, Li C, Fu Y (2020) LatticeNet: Towards Lightweight Image Super-Resolution with Lattice Block, pp 272\u2013289","DOI":"10.1007\/978-3-030-58542-6_17"},{"key":"11007_CR24","doi-asserted-by":"publisher","first-page":"103254","DOI":"10.1016\/j.cviu.2021.103254","volume":"211","author":"Z Li","year":"2021","unstructured":"Li Z, Wang C, Wang J, Ying S, Shi J (2021) Lightweight adaptive weighted network for single image super-resolution. Computer Vision and Image Understanding 211:103254","journal-title":"Computer Vision and Image Understanding"},{"key":"11007_CR25","unstructured":"G.\u00a0A. Mnih\u00a0V, Heess\u00a0N (2014) Recurrent models of visual attention In: Neural Information Processing Systems, pp 2204\u20132212"},{"key":"11007_CR26","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"11007_CR27","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11007_CR28","doi-asserted-by":"crossref","unstructured":"Zhang Y, kunpeng Li, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks In: European Conference on Computer Vision, Vol 11211, pp 294\u2013310","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"11007_CR29","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon I (2018) CBAM: Convolutional Block Attention Module: 15th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part VII, pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"11007_CR30","doi-asserted-by":"publisher","first-page":"3074","DOI":"10.1109\/TMM.2021.3092571","volume":"24","author":"X Zhu","year":"2022","unstructured":"Zhu X, Guo K, Fang H, Chen L, Ren S, Hu B (2022) Cross view capture for stereo image super-resolution. IEEE Transactions on Multimedia 24:3074\u20133086","journal-title":"IEEE Transactions on Multimedia"},{"key":"11007_CR31","doi-asserted-by":"crossref","unstructured":"Han W, Chang S, Liu D, Yu M, Witbrock M, Huang TS (2018) Image super-resolution via dual-state recurrent networks In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 1654\u20131663","DOI":"10.1109\/CVPR.2018.00178"},{"issue":"3","key":"11007_CR32","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TCSVT.2021.3078436","volume":"32","author":"X Zhu","year":"2022","unstructured":"Zhu X, Guo K, Ren S, Hu B, Hu M, Fang H (2022) Lightweight image super-resolution with expectation-maximization attention mechanism. IEEE Transactions on Circuits and Systems for Video Technology 32(3):1273\u20131284","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"11007_CR33","doi-asserted-by":"crossref","unstructured":"Namhyuk\u00a0Ahn BK, Sohn K-A (2018) Fast, accurate, and lightweight super-resolution with cascading residual network In: Computer Vision \u2013 ECCV 2018, Vol 11214, pp 256\u2013272","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"11007_CR34","doi-asserted-by":"crossref","unstructured":"Hui Z, Wang X, Gao X (2018) Fast and accurate single image super-resolution via information distillation network In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 723\u2013731","DOI":"10.1109\/CVPR.2018.00082"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11007-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11007-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11007-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T12:12:40Z","timestamp":1688818360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11007-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,20]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["11007"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11007-0","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2022,8,20]]},"assertion":[{"value":"9 August 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2022","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/Competing interests"}}]}}