{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T04:12:30Z","timestamp":1751515950137,"version":"3.41.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"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":["Pattern Anal Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10044-025-01433-w","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T16:49:15Z","timestamp":1740415755000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MSDBPN: multi-column smoothed dilated convolution based back projection network for stereo image super-resolution"],"prefix":"10.1007","volume":"28","author":[{"given":"Zihao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Lian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"1433_CR1","unstructured":"Han C, Liang J, Wang Q, Rabbani M, Dianat S, Rao R, Wu Y, Liu D (2024) Image translation as diffusion visual programmers. arxiv. Preprint]"},{"key":"1433_CR2","doi-asserted-by":"crossref","unstructured":"Liang J, Cui Y, Wang Q, Geng T, Wang W, Liu D (2024) Clusterfomer: clustering as a universal visual learner. Adv Neural Inform Process Syst 36","DOI":"10.1109\/TNNLS.2025.3531987"},{"key":"1433_CR3","unstructured":"Han C, Lu Y, Sun G, Liang JC, Cao Z, Wang Q, Guan Q, Dianat SA, Rao RM, Geng T, et al (2024) Prototypical transformer as unified motion learners. arXiv preprint arXiv:2406.01559"},{"key":"1433_CR4","doi-asserted-by":"crossref","unstructured":"Han C, Wang Q, Dianat SA, Rabbani M, Rao RM, Fang Y, Guan Q, Huang L, Liu D (2025) Amd: automatic multi-step distillation of large-scale vision models. In: European Conference on Computer Vision, pp. 431\u2013450. Springer","DOI":"10.1007\/978-3-031-73650-6_25"},{"key":"1433_CR5","doi-asserted-by":"crossref","unstructured":"Wang T, Liu Y, Liang JC, Cui Y, Mao Y, Nie S, Liu J, Feng F, Xu Z, Han C, et al (2024) M2 pt: Multimodal prompt tuning for zero-shot instruction learning. arXiv preprint arXiv:2409.15657","DOI":"10.18653\/v1\/2024.emnlp-main.218"},{"key":"1433_CR6","unstructured":"Han C, Wang Q, Cui Y, Wang W, Huang L, Qi S, Liu D (2024) Facing the elephant in the room: Visual prompt tuning or full finetuning? arXiv preprint arXiv:2401.12902"},{"key":"1433_CR7","doi-asserted-by":"crossref","unstructured":"Choi H, Lee J, Yang J (2023) N-gram in swin transformers for efficient lightweight image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2071\u20132081","DOI":"10.1109\/CVPR52729.2023.00206"},{"key":"1433_CR8","doi-asserted-by":"crossref","unstructured":"Li B, Li X, Zhu H, Jin Y, Feng R, Zhang Z, Chen Z (2024) Sed: Semantic-aware discriminator for image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 25784\u201325795","DOI":"10.1109\/CVPR52733.2024.02436"},{"issue":"3","key":"1433_CR9","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s10044-023-01150-2","volume":"26","author":"Y Li","year":"2023","unstructured":"Li Y, Chen H, Li T, Liu B (2023) Ddnsr: a dual-input degradation network for real-world super-resolution. Pattern Anal Appl 26(3):875\u2013888","journal-title":"Pattern Anal Appl"},{"key":"1433_CR10","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"1433_CR11","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"1433_CR12","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"1433_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127426","volume":"580","author":"Y Pan","year":"2024","unstructured":"Pan Y, Tang J, Tjahjadi T (2024) Lpsrgan: Generative adversarial networks for super-resolution of license plate image. Neurocomputing 580:127426","journal-title":"Neurocomputing"},{"key":"1433_CR14","doi-asserted-by":"crossref","unstructured":"Wang L, Wang Y, Liang Z, Lin Z, Yang J, An W, Guo Y (2019) Learning parallax attention for stereo image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12250\u201312259","DOI":"10.1109\/CVPR.2019.01253"},{"key":"1433_CR15","doi-asserted-by":"publisher","first-page":"183672","DOI":"10.1109\/ACCESS.2019.2960561","volume":"7","author":"C Duan","year":"2019","unstructured":"Duan C, Xiao N (2019) Parallax-based spatial and channel attention for stereo image super-resolution. IEEE Access 7:183672\u2013183679","journal-title":"IEEE Access"},{"key":"1433_CR16","doi-asserted-by":"crossref","unstructured":"Xie W, Zhang J, Lu Z, Cao M, Zhao Y (2020) Non-local nested residual attention network for stereo image super-resolution. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2643\u20132647. IEEE","DOI":"10.1109\/ICASSP40776.2020.9054687"},{"issue":"12","key":"1433_CR17","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.1109\/TPAMI.2020.3002836","volume":"43","author":"M Haris","year":"2020","unstructured":"Haris M, Shakhnarovich G, Ukita N (2020) Deep back-projectinetworks for single image super-resolution. IEEE Trans Pattern Anal Mach Intell 43(12):4323\u20134337","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1433_CR18","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13, pp. 184\u2013199. Springer","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"1433_CR19","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"1433_CR20","doi-asserted-by":"crossref","unstructured":"Shuai Y, Wang Y, Peng Y, Xia Y (2018) Accurate image super-resolution using cascaded multi-column convolutional neural networks. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE","DOI":"10.1109\/ICME.2018.8486509"},{"key":"1433_CR21","doi-asserted-by":"crossref","unstructured":"Jeon DS, Baek S-H, Choi I, Kim MH (2018) Enhancing the spatial resolution of stereo images using a parallax prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1721\u20131730","DOI":"10.1109\/CVPR.2018.00185"},{"key":"1433_CR22","first-page":"12031","volume":"34","author":"W Song","year":"2020","unstructured":"Song W, Choi S, Jeong S, Sohn K (2020) Stereoscopic image super-resolution with stereo consistent feature. Proceed AAAI Conf Artif Intell 34:12031\u201312038","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"1433_CR23","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1433_CR24","doi-asserted-by":"crossref","unstructured":"Guo H, Li J, Gao G, Li Z, Zeng T (2023) Pft-ssr: Parallax fusion transformer for stereo image super-resolution. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135. IEEE","DOI":"10.1109\/ICASSP49357.2023.10096174"},{"key":"1433_CR25","doi-asserted-by":"crossref","unstructured":"Lu Z, Li J, Liu H, Huang C, Zhang L, Zeng T (2022) Transformer for single image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 457\u2013466","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"1433_CR26","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1109\/LSP.2020.2973813","volume":"27","author":"X Ying","year":"2020","unstructured":"Ying X, Wang Y, Wang L, Sheng W, An W, Guo Y (2020) A stereo attention module for stereo image super-resolution. IEEE Signal Process Lett 27:496\u2013500","journal-title":"IEEE Signal Process Lett"},{"key":"1433_CR27","doi-asserted-by":"crossref","unstructured":"Haris M, Shakhnarovich G, Ukita N (2019) Recurrent back-projection network for video super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3897\u20133906","DOI":"10.1109\/CVPR.2019.00402"},{"key":"1433_CR28","doi-asserted-by":"crossref","unstructured":"Hosseini H, Xiao B, Jaiswal M, Poovendran R (2017) On the limitation of convolutional neural networks in recognizing negative images. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 352\u2013358. IEEE","DOI":"10.1109\/ICMLA.2017.0-136"},{"issue":"5","key":"1433_CR29","first-page":"1218","volume":"42","author":"H Gao","year":"2019","unstructured":"Gao H, Yuan H, Wang Z, Ji S (2019) Pixel transposed convolutional networks. IEEE Trans Pattern Anal Mach Intell 42(5):1218\u20131227","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1433_CR30","doi-asserted-by":"crossref","unstructured":"Wang Z, Ji S (2018) Smoothed dilated convolutions for improved dense prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2486\u20132495","DOI":"10.1145\/3219819.3219944"},{"key":"1433_CR31","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang L, Yang J, An W, Guo Y (2019) Flickr1024: A large-scale dataset for stereo image super-resolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, pp. 0\u20130","DOI":"10.1109\/ICCVW.2019.00478"},{"key":"1433_CR32","doi-asserted-by":"crossref","unstructured":"Scharstein D, Hirschm\u00fcller H, Kitajima Y, Krathwohl G, Ne\u0161i\u0107 N, Wang X, Westling P (2014) High-resolution stereo datasets with subpixel-accurate ground truth. In: Pattern Recognition: 36th German Conference, GCPR 2014, M\u00fcnster, Germany, September 2-5, 2014, Proceedings 36, pp. 31\u201342. Springer","DOI":"10.1007\/978-3-319-11752-2_3"},{"key":"1433_CR33","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361. IEEE","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"1433_CR34","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3061\u20133070","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"1433_CR35","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"1433_CR36","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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 624\u2013632","DOI":"10.1109\/CVPR.2017.618"},{"key":"1433_CR37","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01433-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-025-01433-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-025-01433-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:42:02Z","timestamp":1751474522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-025-01433-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1433"],"URL":"https:\/\/doi.org\/10.1007\/s10044-025-01433-w","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"assertion":[{"value":"7 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve human or animal subjects, and thus, no ethical approval was required. The study protocol adhered to the guidelines established by the journal.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"53"}}