{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:21:28Z","timestamp":1761582088517,"version":"3.37.3"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s11042-021-10894-0","type":"journal-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T20:03:00Z","timestamp":1620331380000},"page":"26637-26655","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Medical image super-resolution via deep residual neural network in the shearlet domain"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3742-5614","authenticated-orcid":false,"given":"Chunpeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiu","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meihong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun-Qing","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"10894_CR1","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn KA (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: Eur. Conf. Comput. Vis. (ECCV), Munich","DOI":"10.1007\/978-3-030-01249-6_16"},{"issue":"10","key":"10894_CR2","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1109\/TIP.2005.851684","volume":"14","author":"HA Aly","year":"2005","unstructured":"Aly HA, Dubois E (2005) Image up-sampling using total-variation regularization with a new observation model. IEEE Trans Image Process 14(10):1647\u20131659","journal-title":"IEEE Trans Image Process"},{"key":"10894_CR3","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neucom.2019.05.066","volume":"358","author":"F Cao","year":"2019","unstructured":"Cao F, Liu H (2019) Single image super-resolution via multi-scale residual channel attention network. Neurocomputing 358:424\u2013436","journal-title":"Neurocomputing"},{"issue":"7","key":"10894_CR4","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1109\/TIP.2007.896664","volume":"16","author":"GK Chantas","year":"2007","unstructured":"Chantas GK, Galatsanos NP, Woods NA (2007) Super-resolution based on fast registration and maximum a posteriori reconstruction. IEEE Trans Image Process 16(7):1821\u20131830","journal-title":"IEEE Trans Image Process"},{"key":"10894_CR5","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, Li W, Wang Z, Huang Z (2020) RBPNET: An Asymptotic Residual Back-Projection Network for super-resolution of very low-resolution face image. Neurocomputing 376:119\u2013127","journal-title":"Neurocomputing"},{"issue":"6","key":"10894_CR6","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045\u20131057","journal-title":"J Digit Imaging"},{"key":"10894_CR7","doi-asserted-by":"publisher","first-page":"27683","DOI":"10.1007\/s11042-019-07850-4","volume":"78","author":"F Deeba","year":"2019","unstructured":"Deeba F, Kun S, Wang W, Ahmed J, Qadi B (2019) Wavelet integrated residual dictionary training for single image super-resolution. Multimed Tools Appl 78:27683\u201327701","journal-title":"Multimed Tools Appl"},{"key":"10894_CR8","doi-asserted-by":"crossref","unstructured":"Deng X, Yang R, Xu M, Dragotti PL (2019) Wavelet Domain style transfer for an effective perception-distortion tradeoff in single image superresolution. Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Seoul, pp 3076\u20133085","DOI":"10.1109\/ICCV.2019.00317"},{"key":"10894_CR9","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) \u201cLearning a deep convolutional network for image super-resolution. In: Eur. Conf. Comput. Vis. (ECCV), Zurich, pp 184\u2013199","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"10894_CR10","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: Eur. Conf. Comput. Vis. (ECCV), Amsterdam, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"10894_CR11","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.acha.2007.09.003","volume":"25","author":"G Easley","year":"2008","unstructured":"Easley G, Labate D, Lim WQ (2008) Sparse directional image representation using the discrete shearlet transform. Appl Comput Harmon Anal 25:25\u201346","journal-title":"Appl Comput Harmon Anal"},{"key":"10894_CR12","doi-asserted-by":"crossref","unstructured":"Fan DP, Ji GP, Sun G, Cheng MM, Shen J, Shao L (2020) Camouflaged object detection. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Seattle, pp 2777\u20132787","DOI":"10.1109\/CVPR42600.2020.00285"},{"issue":"2","key":"10894_CR13","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"WT Freeman","year":"2002","unstructured":"Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56\u201365","journal-title":"IEEE Comput Graph Appl"},{"key":"10894_CR14","doi-asserted-by":"crossref","unstructured":"Fu X, Huang J, Zeng D, Huang Y, Ding X, Paisley J (2017) Removing rain from single images via a deep detail network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, pp 3855\u20133863","DOI":"10.1109\/CVPR.2017.186"},{"key":"10894_CR15","doi-asserted-by":"publisher","first-page":"4831","DOI":"10.1007\/s11042-018-6751-5","volume":"79","author":"M Gao","year":"2020","unstructured":"Gao M, Han X, Li J, Ji H, Zhang H, Sun J (2020) Image super-resolution based on two-level residual learning CNN. Multimed Tools Appl 79:4831\u20134846","journal-title":"Multimed Tools Appl"},{"key":"10894_CR16","doi-asserted-by":"publisher","first-page":"21815","DOI":"10.1007\/s11042-020-08980-w","volume":"79","author":"YC Gu","year":"2020","unstructured":"Gu YC, Zeng ZT, Chen HB, Wei J, Zhang Y, Chen BH, Li YQ, Qin YJ, Xie Q, Jiang ZR, Lu Y (2020) MedSRGAN: medical images super-resolution using generative adversarial networks,. Multimed Tools Appl 79:21815\u201321840","journal-title":"Multimed Tools Appl"},{"key":"10894_CR17","doi-asserted-by":"crossref","unstructured":"Guo T, Mousavi HS, Vu TH, Monga V (2017) Deep wavelet prediction for image super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), Honolulu, pp 624\u2013632","DOI":"10.1109\/CVPRW.2017.148"},{"key":"10894_CR18","doi-asserted-by":"crossref","unstructured":"Guo T, Mousavi HS, Monga V (2019) Adaptive Transform Domain Image Super-Resolution via Orthogonally Regularized Deep Networks. IEEE Trans Image Process 28(9):4685\u20134700","DOI":"10.1109\/TIP.2019.2913500"},{"key":"10894_CR19","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2018) Deep backprojection networks for super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, pp 1664\u20131673","DOI":"10.1109\/CVPR.2018.00179"},{"issue":"7","key":"10894_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\u2013936","journal-title":"J Opt Soc Am"},{"key":"10894_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Santiago, pp 1026\u2013 1034","DOI":"10.1109\/ICCV.2015.123"},{"key":"10894_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"10894_CR23","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.neucom.2020.03.107","volume":"402","author":"J He","year":"2020","unstructured":"He J, Zheng J, Shen Y, Guo Y, Zhou H (2020) Facial image synthesis and Super-Resolution with stacked generative adversarial network. Neurocomputing 402:359\u2013365","journal-title":"Neurocomputing"},{"key":"10894_CR24","doi-asserted-by":"crossref","unstructured":"Huang H, He R, Sun Z, Tan T (2017) Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, pp 1689\u20131697","DOI":"10.1109\/ICCV.2017.187"},{"key":"10894_CR25","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, Tan T (2019) Wavelet domain generative adversarial network for multi-scale face hallucination. Int J Comput Vis 127:763\u2013784","journal-title":"Int J Comput Vis"},{"key":"10894_CR26","doi-asserted-by":"crossref","unstructured":"Hui Z, Wang X, Gao X (2018) Fast and accurate single image super-resolution via information distillation network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, pp 723\u2013731","DOI":"10.1109\/CVPR.2018.00082"},{"key":"10894_CR27","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proc. Int. Conf. Mach. Learn. (ICML), Lille, pp 448\u2013456"},{"key":"10894_CR28","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neucom.2019.06.102","volume":"370","author":"X Jin","year":"2019","unstructured":"Jin X, Xiong Q, Xiong C, Li Z, Gao Z (2019) Single image super-resolution with multi-level feature fusion recursive network. Neurocomputing 370:166\u2013173","journal-title":"Neurocomputing"},{"key":"10894_CR29","doi-asserted-by":"crossref","unstructured":"Kim J, Kwon Lee J, Mu Lee, K (2016) Accurate image super-resolution using very deep convolutional networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, pp 1646\u2013 1654","DOI":"10.1109\/CVPR.2016.182"},{"key":"10894_CR30","doi-asserted-by":"crossref","unstructured":"Kim J, Kwon Lee J, Mu Lee, K (2016) Deeply-recursive convolutional network for image super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"10894_CR31","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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, pp 624\u2013632","DOI":"10.1109\/CVPR.2017.618"},{"key":"10894_CR32","doi-asserted-by":"crossref","unstructured":"Le TN, Nguyen TV, Nie Z, Tran MT, Sugimoto A (2019) Anabranch network for camouflaged object segmentation. Comput Vis Image Underst 184:45\u201356","DOI":"10.1016\/j.cviu.2019.04.006"},{"key":"10894_CR33","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszr 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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, pp 4681\u20134690","DOI":"10.1109\/CVPR.2017.19"},{"issue":"5","key":"10894_CR34","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.jvcir.2009.03.008","volume":"20","author":"X Li","year":"2009","unstructured":"Li X, Lam KM, Qiu G, Shen L, Wang S (2009) Example-based image super-resolution with class-specific predictors. J Vis Commun Image Represent 20(5):312\u2013322","journal-title":"J Vis Commun Image Represent"},{"key":"10894_CR35","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.neucom.2019.02.067","volume":"398","author":"D Lin","year":"2020","unstructured":"Lin D, Xu G, Xu W, Wang YX, Fu K (2020) SCRSR: An efficient recursive convolutional neural network for fast and accurate image super-resolution. Neurocomputing 398:399\u2013407","journal-title":"Neurocomputing"},{"key":"10894_CR36","doi-asserted-by":"publisher","first-page":"2525","DOI":"10.1007\/s11042-018-6386-6","volume":"78","author":"YN Liu","year":"2019","unstructured":"Liu YN, Zhang SS, Sang Y, Wang SM (2019) Improving image retrieval by integrating shape and texture features. Multimed Tools Appl 78:2525\u20132550","journal-title":"Multimed Tools Appl"},{"key":"10894_CR37","doi-asserted-by":"crossref","unstructured":"Liu YN, Zhao L, Zhang SS, Yang J (2020) Hybrid resolution network using edge guided region mutual information loss for human parsing. ACM Multimedia, Seattle, pp 1670\u20131678","DOI":"10.1145\/3394171.3413831"},{"key":"10894_CR38","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.1109\/TIP.2021.3055737","volume":"30","author":"Y Liu","year":"2021","unstructured":"Liu Y, Zhang S, Xu J, Yang J, Tai Y-W (2021) An accurate and lightweight method for human body image super-resolution. IEEE Trans Image Process 30:2888\u20132897","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"10894_CR39","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1109\/TIFS.2016.2566261","volume":"11","author":"B Ma","year":"2016","unstructured":"Ma B, Shi YQ (2016) A reversible data hiding scheme based on code division multiplexing. IEEE Trans Inf Forensic Secur 11(9):1914\u20131927","journal-title":"IEEE Trans Inf Forensic Secur"},{"key":"10894_CR40","unstructured":"Mao X, Shen C, Yang Y-B (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Adv. Neural Inf. Process. Syst. (NIPS), Barcelona, pp 2802\u20132810"},{"issue":"3","key":"10894_CR41","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","volume":"20","author":"SC Park","year":"2003","unstructured":"Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag 20(3):21\u201336","journal-title":"IEEE Signal Process. Mag"},{"key":"10894_CR42","doi-asserted-by":"crossref","unstructured":"Qiu Y, Wang R, Tao D, Cheng J (2019) Embedded block residual network: a recursive restoration model for Single-Image Super-Resolution. Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Seoul, pp 4179\u2013 4188","DOI":"10.1109\/ICCV.2019.00428"},{"issue":"10","key":"10894_CR43","doi-asserted-by":"publisher","first-page":"1817","DOI":"10.1109\/TIP.2008.2002833","volume":"17","author":"A Sanchez-Beato","year":"2008","unstructured":"Sanchez-Beato A, Pajares G (2008) Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image. IEEE Trans Image Process 17(10):1817\u20131826","journal-title":"IEEE Trans Image Process"},{"key":"10894_CR44","doi-asserted-by":"crossref","unstructured":"Sang Y, Sun J, Wang S, Li K, Qi H (2020) Medical image Super-Resolution via granular Multi-Scale network in NSCT domain. IEEE Int. Conf. on Multimedia and Expo (ICME), London, pp","DOI":"10.1109\/ICME46284.2020.9102831"},{"key":"10894_CR45","doi-asserted-by":"crossref","unstructured":"Sang Y, Sun J, Wang S, Peng Y, Zhang X, Yang Z (2019) Multi-scale information distillation network for image super resolution in NSCT domain. Int. Conf. on Neural Information Processing (ICONIP), Sydney, pp 50\u201359","DOI":"10.1007\/978-3-030-36711-4_5"},{"key":"10894_CR46","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.eswa.2019.03.032","volume":"128","author":"F Sha","year":"2019","unstructured":"Sha F, Zandavi SM, Chung YY (2019) Fast deep parallel residual network for accurate super resolution image processing. Expert Syst Appl 128:157\u2013168","journal-title":"Expert Syst Appl"},{"key":"10894_CR47","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Huszr 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: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Las Vegas, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"10894_CR48","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Honolulu, pp 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"},{"key":"10894_CR49","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X, Xu C (2017) Memnet: A persistent memory network for image restoration. In: Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Venice, pp 4539\u20134547","DOI":"10.1109\/ICCV.2017.486"},{"issue":"4","key":"10894_CR50","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s10278-017-0033-z","volume":"31","author":"K Umehara","year":"2018","unstructured":"Umehara K, Ota J, Ishida T (2018) Application of super-resolution convolutional neural network for enhancing image resolution in chest CT. J Digit Imaging 31(4):441\u2013450","journal-title":"J Digit Imaging"},{"issue":"4","key":"10894_CR51","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"10894_CR52","doi-asserted-by":"crossref","unstructured":"Wang C, Wang S, Ma B, Li J, Dong X, Xia Z (2019) Transform domain based medical image super-resolution via deep multi-scale network. Proc. of IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Brighton, pp 2387\u20132391","DOI":"10.1109\/ICASSP.2019.8682288"},{"issue":"12","key":"10894_CR53","doi-asserted-by":"publisher","first-page":"4440","DOI":"10.1109\/TCSVT.2019.2960507","volume":"30","author":"C Wang","year":"2020","unstructured":"Wang C, Wang X, Xia Z, Ma B, Shi Y (2020) Image description with polar harmonic fourier moments. IEEE Trans Circ Syst Video Technol 30 (12):4440\u20134452","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"10894_CR54","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.ins.2018.08.028","volume":"470","author":"C Wang","year":"2019","unstructured":"Wang C, Wang X, Xia Z, Zhang C (2019) Ternary radial harmonic Fourier moments based robust stereo image zero-watermarking algorithm. Inf Sci 470:109\u2013120","journal-title":"Inf Sci"},{"key":"10894_CR55","doi-asserted-by":"crossref","unstructured":"Xu W, Song H, Zhang K, Liu Q, Liu J (2020) Learning lightweight Multi-Scale Feedback Residual network for single image super-resolution. Comput Vis Image Underst 197-198:103005","DOI":"10.1016\/j.cviu.2020.103005"},{"key":"10894_CR56","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Eur. Conf. Comput. Vis. (ECCV), Munich, pp 294\u2013310","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"10894_CR57","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Salt Lake City, pp 2472\u20132481","DOI":"10.1109\/CVPR.2018.00262"},{"key":"10894_CR58","unstructured":"Zhong Z, Shen T, Yang Y, Zhang C, Lin Z (2018) Joint sub-bands learning with clique structures for wavelet domain super-resolution. Neural Information Processing Systems (NeurIPS), Montral, pp 165\u2013175"},{"issue":"7","key":"10894_CR59","doi-asserted-by":"publisher","first-page":"3312","DOI":"10.1109\/TIP.2012.2189576","volume":"21","author":"F Zhou","year":"2012","unstructured":"Zhou F, Yang W, Liao Q (2012) Interpolation-based image super-resolution using multisurface fitting. IEEE Trans Image Process 21(7):3312\u20133318","journal-title":"IEEE Trans Image Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10894-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10894-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10894-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T06:26:07Z","timestamp":1625552767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10894-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,6]]},"references-count":59,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10894"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10894-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2021,5,6]]},"assertion":[{"value":"27 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","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":"<!--Emphasis Type='Bold' removed-->Competing interests"}}]}}