{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T08:38:33Z","timestamp":1775205513496,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"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":["62572420, 62273290,61572419"],"award-info":[{"award-number":["62572420, 62273290,61572419"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-08098-6","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T17:12:29Z","timestamp":1764177149000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid CNN-Transformer network with multi-scale attention for enhanced image compressive sensing"],"prefix":"10.1007","volume":"81","author":[{"given":"Yaqin","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinglei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"issue":"2","key":"8098_CR1","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/TIT.2005.862083","volume":"52","author":"EJ Cand\u00e8s","year":"2006","unstructured":"Cand\u00e8s EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489\u2013509","journal-title":"IEEE Trans Inf Theory"},{"issue":"6","key":"8098_CR2","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/mrm.21391","volume":"58","author":"M Lustig","year":"2007","unstructured":"Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182\u20131195","journal-title":"Magn Reson Med"},{"issue":"2","key":"8098_CR3","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/JSTSP.2009.2039181","volume":"4","author":"VM Patel","year":"2010","unstructured":"Patel VM, Easley GR, Healy DM, Chellappa R (2010) Compressed synthetic aperture radar. IEEE J Sel Top Signal Process 4(2):244\u2013254","journal-title":"IEEE J Sel Top Signal Process"},{"key":"8098_CR4","doi-asserted-by":"crossref","unstructured":"Luo C, Wu F, Sun J, Chen CW (2009) Compressive data gathering for large-scale wireless sensor networks. In: Proceedings of the 15th Annual International Conference on Mobile Computing and Networking, pp 145\u2013156","DOI":"10.1145\/1614320.1614337"},{"issue":"1","key":"8098_CR5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1137\/S003614450037906X","volume":"43","author":"SS Chen","year":"2001","unstructured":"Chen SS, Donoho DL, Saunders MA (2001) Atomic decomposition by basis pursuit. SIAM Rev 43(1):129\u2013159","journal-title":"SIAM Rev"},{"issue":"12","key":"8098_CR6","doi-asserted-by":"publisher","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","volume":"53","author":"JA Tropp","year":"2007","unstructured":"Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655\u20134666","journal-title":"IEEE Trans Inf Theory"},{"key":"8098_CR7","doi-asserted-by":"crossref","unstructured":"Zhang J, Ghanem B (2018) ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1828\u20131837","DOI":"10.1109\/CVPR.2018.00196"},{"key":"8098_CR8","doi-asserted-by":"crossref","unstructured":"Kulkarni K, Lohit S, Turaga P, Kerviche R, Ashok A (2016) Reconnet: Non-iterative reconstruction of images from compressively sensed measurements. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 449\u2013458","DOI":"10.1109\/CVPR.2016.55"},{"key":"8098_CR9","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1109\/TIP.2019.2928136","volume":"29","author":"W Shi","year":"2020","unstructured":"Shi W, Jiang F, Liu S, Zhao D (2020) Image compressed sensing using convolutional neural network. IEEE Trans Image Process 29:375\u2013388","journal-title":"IEEE Trans Image Process"},{"key":"8098_CR10","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1109\/TIP.2020.3044472","volume":"30","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Liu Y, Liu J, Wen F, Zhu C (2021) AMP-Net: denoising-based deep unfolding for compressive image sensing. IEEE Trans Image Process 30:1487\u20131500","journal-title":"IEEE Trans Image Process"},{"key":"8098_CR11","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2021) An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations"},{"key":"8098_CR12","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"8098_CR13","doi-asserted-by":"publisher","unstructured":"Guo Z, Gan H (2024) CPP-Net: Embracing multi-scale feature fusion into deep unfolding CP-PPA network for compressive sensing. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 25086\u201325095. https:\/\/doi.org\/10.1109\/CVPR52733.2024.02370","DOI":"10.1109\/CVPR52733.2024.02370"},{"key":"8098_CR14","doi-asserted-by":"publisher","first-page":"2953","DOI":"10.1109\/TMM.2023.3305828","volume":"26","author":"X Kong","year":"2024","unstructured":"Kong X, Chen Y, He Z (2024) When channel correlation meets sparse prior: keeping interpretability in image compressive sensing. IEEE Trans Multimedia 26:2953\u20132965","journal-title":"IEEE Trans Multimedia"},{"issue":"9","key":"8098_CR15","doi-asserted-by":"publisher","first-page":"4949","DOI":"10.1109\/TCYB.2024.3363748","volume":"54","author":"M Shen","year":"2024","unstructured":"Shen M, Gan H, Ma C, Ning C, Li H, Liu F (2024) MTC-CSNet: marrying transformer and convolution for image compressed sensing. IEEE Trans Cybern 54(9):4949\u20134961","journal-title":"IEEE Trans Cybern"},{"key":"8098_CR16","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s40747-025-01963-0","volume":"11","author":"J Zhang","year":"2025","unstructured":"Zhang J, Huang W, Lu M, Li L, Shen Y, Wang Y, Du J (2025) Compressed sensing transformer unfolding network for high resolution image denoising. Complex Intell Syst 11:336. https:\/\/doi.org\/10.1007\/s40747-025-01963-0","journal-title":"Complex Intell Syst"},{"issue":"3","key":"8098_CR17","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1109\/TCI.2018.2846413","volume":"4","author":"S Lohit","year":"2018","unstructured":"Lohit S, Kulkarni K, Kerviche R, Turaga P, Ashok A (2018) Convolutional neural networks for noniterative reconstruction of compressively sensed images. IEEE Trans Comput Imaging 4(3):326\u2013340","journal-title":"IEEE Trans Comput Imaging"},{"key":"8098_CR18","doi-asserted-by":"crossref","unstructured":"Mousavi A, Patel AB, Baraniuk RG (2015) A deep learning approach to structured signal recovery. In: 53rd Annual Allerton Conference on Communication, Control, and Computing, pp 1336\u20131343","DOI":"10.1109\/ALLERTON.2015.7447163"},{"issue":"4","key":"8098_CR19","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1109\/JSTSP.2020.2977507","volume":"14","author":"J Zhang","year":"2020","unstructured":"Zhang J, Zhao C, Gao W (2020) Optimization-inspired compact deep compressive sensing. IEEE J Sel Top Signal Process 14(4):765\u2013774","journal-title":"IEEE J Sel Top Signal Process"},{"key":"8098_CR20","doi-asserted-by":"publisher","first-page":"6991","DOI":"10.1109\/TIP.2022.3217365","volume":"31","author":"M Shen","year":"2022","unstructured":"Shen M, Gan H, Ning C, Hua Y, Zhang T (2022) TransCS: a transformer-based hybrid architecture for image compressed sensing. IEEE Trans Image Process 31:6991\u20137005","journal-title":"IEEE Trans Image Process"},{"key":"8098_CR21","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1109\/TIP.2023.3274988","volume":"32","author":"D Ye","year":"2023","unstructured":"Ye D, Ni Z, Wang H, Zhang J, Wang S, Kwong S (2023) CSformer: bridging convolution and transformer for compressive sensing. IEEE Trans Image Process 32:2827\u20132842. https:\/\/doi.org\/10.1109\/TIP.2023.3274988","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"8098_CR22","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.3390\/s25041026","volume":"25","author":"X Gao","year":"2025","unstructured":"Gao X, Chen B, Yao X, Yuan Y (2025) SSM-NET: enhancing compressed sensing image reconstruction with mamba architecture and fast iterative shrinking threshold algorithm optimization. Sensors 25(4):1026. https:\/\/doi.org\/10.3390\/s25041026","journal-title":"Sensors"},{"key":"8098_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113793","volume":"322","author":"W Ding","year":"2025","unstructured":"Ding W, Xu D, Shi J, Qian Q (2025) RDFT-CDUN: image compressed sensing using complex-valued measurement matrix and complex-based deep unfolding network. Knowl-Based Syst 322:113793. https:\/\/doi.org\/10.1016\/j.knosys.2025.113793","journal-title":"Knowl-Based Syst"},{"key":"8098_CR24","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"8098_CR25","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":"8098_CR26","doi-asserted-by":"publisher","first-page":"5676","DOI":"10.1109\/TMM.2022.3198323","volume":"25","author":"K Zhang","year":"2023","unstructured":"Zhang K, Hua Z, Li Y, Chen Y, Zhou Y (2023) Ams-net: adaptive multi-scale network for image compressive sensing. IEEE Trans Multimedia 25:5676\u20135689","journal-title":"IEEE Trans Multimedia"},{"key":"8098_CR27","doi-asserted-by":"crossref","unstructured":"Song J, Mou C, Wang S, Ma S, Zhang J (2023) Optimization-inspired cross-attention transformer for compressive sensing. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 6174\u20136184","DOI":"10.1109\/CVPR52729.2023.00598"},{"key":"8098_CR28","volume-title":"A Wavelet Tour of Signal Processing: The Sparse Way","author":"S Mallat","year":"2009","unstructured":"Mallat S (2009) A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, New York"},{"issue":"8","key":"8098_CR29","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","volume":"35","author":"J Bruna","year":"2013","unstructured":"Bruna J, Mallat S (2013) Invariant scattering convolution networks. IEEE Trans Pattern Anal Mach Intell 35(8):1872\u20131886","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7553","key":"8098_CR30","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"8098_CR31","doi-asserted-by":"crossref","unstructured":"Lin T.Y, Maire M, Belongie S, Bourdev L, Girshick R, Hays J, Perona P, Ramanan D, Zitnick CL, Doll\u00e1r P (2015) Microsoft COCO: Common Objects in Context","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"8098_CR32","doi-asserted-by":"crossref","unstructured":"Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: International Conference on Curves and Surfaces, pp 711\u2013730","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"8098_CR33","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference, pp 135\u2013113510","DOI":"10.5244\/C.26.135"},{"key":"8098_CR34","doi-asserted-by":"crossref","unstructured":"Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"8098_CR35","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision, pp 416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"8098_CR36","doi-asserted-by":"crossref","unstructured":"Mou C, Wang Q, Zhang J (2022) Deep generalized unfolding networks for image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 17399\u201317410","DOI":"10.1109\/CVPR52688.2022.01688"},{"key":"8098_CR37","doi-asserted-by":"publisher","unstructured":"Chen W, Yang C, Yang X (2022) Fsoinet: Feature-space optimization-inspired network for image compressive sensing. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 2460\u20132464. https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746648","DOI":"10.1109\/ICASSP43922.2022.9746648"},{"issue":"4","key":"8098_CR38","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1109\/TCYB.2021.3127657","volume":"53","author":"H Gan","year":"2023","unstructured":"Gan H, Gao Y, Liu C, Chen H, Zhang T, Liu F (2023) AutoBCS: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction. IEEE Trans Cybern 53(4):2558\u20132571. https:\/\/doi.org\/10.1109\/TCYB.2021.3127657","journal-title":"IEEE Trans Cybern"},{"key":"8098_CR39","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/TIP.2023.3263100","volume":"32","author":"J Song","year":"2023","unstructured":"Song J, Chen B, Zhang J (2023) Dynamic path-controllable deep unfolding network for compressive sensing. IEEE Trans Image Process 32:2202\u20132214","journal-title":"IEEE Trans Image Process"},{"key":"8098_CR40","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1109\/TCI.2023.3244396","volume":"9","author":"H Gan","year":"2023","unstructured":"Gan H, Shen M, Hua Y, Ma C, Zhang T (2023) From patch to pixel: a transformer-based hierarchical framework for compressive image sensing. IEEE Trans Comput Imaging 9:133\u2013146. https:\/\/doi.org\/10.1109\/TCI.2023.3244396","journal-title":"IEEE Trans Comput Imaging"},{"key":"8098_CR41","doi-asserted-by":"crossref","unstructured":"Shi W, Jiang F, Liu S, Zhao D (2019) Scalable convolutional neural network for image compressed sensing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 12290\u201312299","DOI":"10.1109\/CVPR.2019.01257"},{"key":"8098_CR42","doi-asserted-by":"publisher","first-page":"9482","DOI":"10.1109\/TIP.2020.3023629","volume":"29","author":"Y Sun","year":"2020","unstructured":"Sun Y, Chen J, Liu Q, Liu B, Guo G (2020) Dual-path attention network for compressed sensing image reconstruction. IEEE Trans Image Process 29:9482\u20139495","journal-title":"IEEE Trans Image Process"},{"key":"8098_CR43","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1109\/TMM.2020.3014561","volume":"23","author":"S Zhou","year":"2021","unstructured":"Zhou S, He Y, Liu Y, Li C, Zhang J (2021) Multi-channel deep networks for block-based image compressive sensing. IEEE Trans Multimedia 23:2627\u20132640","journal-title":"IEEE Trans Multimedia"},{"key":"8098_CR44","doi-asserted-by":"publisher","first-page":"5412","DOI":"10.1109\/TIP.2022.3195319","volume":"31","author":"B Chen","year":"2022","unstructured":"Chen B, Zhang J (2022) Content-aware scalable deep compressed sensing. IEEE Trans Image Process 31:5412\u20135426","journal-title":"IEEE Trans Image Process"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08098-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08098-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08098-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T17:12:32Z","timestamp":1764177152000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08098-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":44,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["8098"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08098-6","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"16 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 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":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1598"}}