{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:03:51Z","timestamp":1778688231309,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"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":["No.U1903213"],"award-info":[{"award-number":["No.U1903213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18284-y","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T06:01:47Z","timestamp":1706162507000},"page":"67231-67249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform"],"prefix":"10.1007","volume":"83","author":[{"given":"Tongguan","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guxue","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaocong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8747-1790","authenticated-orcid":false,"given":"Huicheng","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"18284_CR1","doi-asserted-by":"crossref","unstructured":"Chen Y, Xia R, Zou K, et al (2023) FFTI: Image inpainting algorithm via features fusion and two-steps inpainting[J]. J Vis Commun Image Represent 91:103776","DOI":"10.1016\/j.jvcir.2023.103776"},{"key":"18284_CR2","doi-asserted-by":"publisher","first-page":"4367","DOI":"10.1007\/s10489-020-02116-1","volume":"51","author":"Y Chen","year":"2021","unstructured":"Chen Y, Liu L, Phonevilay V et al (2021) Image super-resolution reconstruction based on feature map attention mechanism[J]. Appl Intell 51:4367\u20134380","journal-title":"Appl Intell"},{"key":"18284_CR3","unstructured":"Baker S, Kanade T (2000) Hallucinating faces[C]\/\/Proceedings fourth IEEE international conference on automatic face and gesture recognition (Cat. No. PR00580). IEEE 83\u201388"},{"issue":"1","key":"18284_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3485132","volume":"55","author":"J Jiang","year":"2021","unstructured":"Jiang J, Wang C, Liu X et al (2021) Deep learning-based face super-resolution: A survey[J]. ACM Comput Surv (CSUR) 55(1):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"key":"18284_CR5","doi-asserted-by":"crossref","unstructured":"Chen Y, Tai Y, Liu X et al (2018) Fsrnet: End-to-end learning face super-resolution with facial priors[C]\/\/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2492\u20132501","DOI":"10.1109\/CVPR.2018.00264"},{"key":"18284_CR6","doi-asserted-by":"crossref","unstructured":"Zhang Y, Wu Y, Chen L (2020) MSFSR: A multi-stage face super-resolution with accurate facial representation via enhanced facial boundaries[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops 504\u2013505","DOI":"10.1109\/CVPRW50498.2020.00260"},{"key":"18284_CR7","doi-asserted-by":"crossref","unstructured":"Ma C, Jiang Z, Rao Y et al (2020) Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 5569\u20135578","DOI":"10.1109\/CVPR42600.2020.00561"},{"key":"18284_CR8","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neucom.2019.09.079","volume":"376","author":"X Chen","year":"2020","unstructured":"Chen X, Wang X, Lu Y et al (2020) RBPNET: An asymptotic residual back-projection network for super-resolution of very low-resolution face image[J]. Neurocomputing 376:119\u2013127","journal-title":"Neurocomputing"},{"key":"18284_CR9","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neucom.2021.03.048","volume":"446","author":"J Kim","year":"2021","unstructured":"Kim J, Li G, Yun I et al (2021) Edge and identity preserving network for face super-resolution[J]. Neurocomputing 446:11\u201322","journal-title":"Neurocomputing"},{"key":"18284_CR10","doi-asserted-by":"publisher","first-page":"106987","DOI":"10.1016\/j.knosys.2021.106987","volume":"222","author":"H Wang","year":"2021","unstructured":"Wang H, Hu Q, Wu C et al (2021) Dclnet: Dual closed-loop networks for face super-resolution[J]. Knowl-Based Syst 222:106987","journal-title":"Knowl-Based Syst"},{"key":"18284_CR11","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neunet.2022.04.026","volume":"152","author":"C Zhuang","year":"2022","unstructured":"Zhuang C, Li M, Zhang K et al (2022) Multi-level landmark-guided deep network for face super-resolution[J]. Neural Netw 152:276\u2013286","journal-title":"Neural Netw"},{"key":"18284_CR12","doi-asserted-by":"crossref","unstructured":"Cao Q, Lin L, Shi Y et al (2017) Attention-aware face hallucination via deep reinforcement learning[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition 690\u2013698","DOI":"10.1109\/CVPR.2017.180"},{"issue":"10","key":"18284_CR13","doi-asserted-by":"publisher","first-page":"2734","DOI":"10.1109\/TMM.2019.2960586","volume":"22","author":"K Jiang","year":"2019","unstructured":"Jiang K, Wang Z, Yi P et al (2019) ATMFN: Adaptive-threshold-based multi-model fusion network for compressed face hallucination[J]. IEEE Trans Multimedia 22(10):2734\u20132747","journal-title":"IEEE Trans Multimedia"},{"key":"18284_CR14","unstructured":"Jiang K, Wang Z, Yi P et al (2020) Dual-path deep fusion network for face image hallucination[J]. IEEE Trans Neural Netw Learn Syst"},{"key":"18284_CR15","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.neucom.2020.01.015","volume":"387","author":"T Lu","year":"2020","unstructured":"Lu T, Wang J, Jiang J et al (2020) Global-local fusion network for face super-resolution[J]. Neurocomputing 387:309\u2013320","journal-title":"Neurocomputing"},{"key":"18284_CR16","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1109\/TIP.2020.3043093","volume":"30","author":"C Chen","year":"2020","unstructured":"Chen C, Gong D, Wang H et al (2020) Learning spatial attention for face super-resolution[J]. IEEE Trans Image Process 30:1219\u20131231","journal-title":"IEEE Trans Image Process"},{"key":"18284_CR17","doi-asserted-by":"crossref","unstructured":"Wang Y, Lu T, Zhang Y, et al (2021) Tanet: a new paradigm for global face super-resolution via transformer-cnn aggregation network[J]. arXiv preprint arXiv:2109.08174","DOI":"10.1109\/RCAE53607.2021.9638780"},{"key":"18284_CR18","doi-asserted-by":"crossref","unstructured":"Gao G, Xu Z, Li J, et al (2023) Ctcnet: a cnn-transformer cooperation network for face image super-resolution[J]. IEEE Trans Image Process 32:1978-1991","DOI":"10.1109\/TIP.2023.3261747"},{"key":"18284_CR19","doi-asserted-by":"crossref","unstructured":"Jiang J, Wang C, Liu X, et al (2021) Spectral splitting and aggregation network for hyperspectral face super-resolution[J]. arXiv preprint arXiv:2108.13584","DOI":"10.1109\/CVPRW56347.2022.00041"},{"key":"18284_CR20","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.patcog.2016.11.015","volume":"64","author":"Y Duan","year":"2017","unstructured":"Duan Y, Liu F, Jiao L et al (2017) SAR image segmentation based on convolutional-wavelet neural network and Markov random field[J]. Pattern Recogn 64:255\u2013267","journal-title":"Pattern Recogn"},{"key":"18284_CR21","doi-asserted-by":"crossref","unstructured":"Li Q, Shen L, Guo S et al (2020) Wavelet integrated CNNs for noise-robust image classification[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 7245\u20137254","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"18284_CR22","doi-asserted-by":"publisher","first-page":"53392","DOI":"10.1109\/ACCESS.2021.3070809","volume":"9","author":"C Sun","year":"2021","unstructured":"Sun C, Lai H, Wang L et al (2021) Efficient attention fusion network in wavelet domain for demoireing[J]. IEEE Access 9:53392\u201353400","journal-title":"IEEE Access"},{"key":"18284_CR23","doi-asserted-by":"crossref","unstructured":"Huang H, He R, Sun Z et al (2017) Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution[C]\/\/Proceedings of the IEEE international conference on computer vision. 1689\u20131697","DOI":"10.1109\/ICCV.2017.187"},{"issue":"6","key":"18284_CR24","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1007\/s11263-019-01154-8","volume":"127","author":"H Huang","year":"2019","unstructured":"Huang H, He R, Sun Z et al (2019) Wavelet domain generative adversarial network for multi-scale face hallucination[J]. Int J Comput Vision 127(6):763\u2013784","journal-title":"Int J Comput Vision"},{"issue":"7","key":"18284_CR25","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1007\/s00371-020-01925-2","volume":"37","author":"L Ying","year":"2021","unstructured":"Ying L, Dinghua S, Fuping W et al (2021) Learning wavelet coefficients for face super-resolution[J]. Vis Comput 37(7):1613\u20131622","journal-title":"Vis Comput"},{"key":"18284_CR26","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation[C]\/\/International conference on medical image computing and computer-assisted intervention. Springer, Cham 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"18284_CR27","unstructured":"Cai J, Gu S, Timofte R et al (2019) Ntire 2019 challenge on real image super-resolution: Methods and results[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops. 0\u20130"},{"key":"18284_CR28","doi-asserted-by":"crossref","unstructured":"Feng R, Gu J, Qiao Y et al (2019) Suppressing model overfitting for image super-resolution networks[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops. 0\u20130","DOI":"10.1109\/CVPRW.2019.00248"},{"key":"18284_CR29","first-page":"1","volume":"19","author":"MS Dey","year":"2021","unstructured":"Dey MS, Chaudhuri U, Banerjee B et al (2021) Dual-path Morph-UNet for road and building segmentation from satellite images[J]. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"18284_CR30","first-page":"1","volume":"60","author":"C Zhang","year":"2022","unstructured":"Zhang C, Wang L, Cheng S et al (2022) SwinSUNet: Pure transformer network for remote sensing image change detection[J]. IEEE Trans Geosci Remote Sens 60:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"18284_CR31","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"18284_CR32","doi-asserted-by":"crossref","unstructured":"Hui Z, Gao X, Yang Y et al (2019) Lightweight image super-resolution with information multi-distillation network[C]\/\/Proceedings of the 27th acm international conference on multimedia. 2024\u20132032","DOI":"10.1145\/3343031.3351084"},{"key":"18284_CR33","unstructured":"Li Y, Zhang K, Timofte R et al (2022) Ntire 2022 challenge on efficient super-resolution: Methods and results[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 1062\u20131102"},{"issue":"6","key":"18284_CR34","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"R Keys","year":"1981","unstructured":"Keys R (1981) Cubic convolution interpolation for digital image processing[J]. IEEE Trans Acoust Speech Signal Process 29(6):1153\u20131160","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"18284_CR35","unstructured":"Howard A G, Zhu M, Chen B, et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv:1704.04861"},{"key":"18284_CR36","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks[C]\/\/International conference on machine learning. PMLR. 6105\u20136114"},{"key":"18284_CR37","doi-asserted-by":"crossref","unstructured":"Chen L, Chu X, Zhang X, et al (2022) Simple baselines for image restoration[C]\/\/European Conference on Computer Vision. Cham: Springer Nature Switzerland 17-33","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"18284_CR38","doi-asserted-by":"crossref","unstructured":"Chu X, Chen L, Yu W (2022) NAFSSR: Stereo image super-resolution using NAFNet[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 1239\u20131248","DOI":"10.1109\/CVPRW56347.2022.00130"},{"key":"18284_CR39","doi-asserted-by":"crossref","unstructured":"Liu J, Tang J, Wu G (2020) Residual feature distillation network for lightweight image super-resolution[C]\/\/European Conference on Computer Vision. Springer, Cham 41\u201355","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"18284_CR40","doi-asserted-by":"crossref","unstructured":"Cai Y, Lai H, Jia Z, et al (2022) Lightweight spatial-channel adaptive coordination of multilevel refinement enhancement network for image reconstruction[J]. Knowl.-Based Syst 256:109824","DOI":"10.1016\/j.knosys.2022.109824"},{"key":"18284_CR41","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Wang X et al (2015) Deep learning face attributes in the wild[C]\/\/Proceedings of the IEEE international conference on computer vision. 3730\u20133738","DOI":"10.1109\/ICCV.2015.425"},{"issue":"10","key":"18284_CR42","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z et al (2016) Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"18284_CR43","doi-asserted-by":"crossref","unstructured":"Le V, Brandt J, Lin Z et al (2012) Interactive facial feature localization[C]\/\/European conference on computer vision. Springer, Berlin, Heidelberg 679\u2013692","DOI":"10.1007\/978-3-642-33712-3_49"},{"key":"18284_CR44","doi-asserted-by":"crossref","unstructured":"Lu T, Wang Y, Zhang Y et al (2021) Face hallucination via split-attention in split-attention network[C]\/\/Proceedings of the 29th ACM international conference on multimedia. 5501\u20135509","DOI":"10.1145\/3474085.3475682"},{"issue":"11","key":"18284_CR45","doi-asserted-by":"publisher","first-page":"7317","DOI":"10.1109\/TCSVT.2022.3181828","volume":"32","author":"C Wang","year":"2022","unstructured":"Wang C, Jiang J, Zhong Z et al (2022) Propagating facial prior knowledge for multitask learning in face super-resolution[J]. IEEE Trans Circuits Syst Video Technol 32(11):7317\u20137331","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"18284_CR46","doi-asserted-by":"crossref","unstructured":"Huang W, Lan S, Wang W et al (2022) Face super-resolution with spatial attention guided by multiscale receptive-field features[C]\/\/Artificial neural networks and machine learning\u2013ICANN 2022: 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6\u20139, 2022, Proceedings, Part I. Cham: Springer International Publishing 145\u2013157","DOI":"10.1007\/978-3-031-15919-0_13"},{"key":"18284_CR47","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.neucom.2021.03.124","volume":"449","author":"S Liu","year":"2021","unstructured":"Liu S, Xiong C, Shi X et al (2021) Progressive face super-resolution with cascaded recurrent convolutional network[J]. Neurocomputing 449:357\u2013367","journal-title":"Neurocomputing"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18284-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18284-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18284-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T10:32:41Z","timestamp":1720521161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18284-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"references-count":47,"journal-issue":{"issue":"25","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["18284"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18284-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]},"assertion":[{"value":"22 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2024","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 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":"Competing interest"}}]}}