{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:08:53Z","timestamp":1778782133807,"version":"3.51.4"},"reference-count":76,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272376"],"award-info":[{"award-number":["62272376"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["202DLGY04-05"],"award-info":[{"award-number":["202DLGY04-05"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["S2021-YF-YBSF-0094"],"award-info":[{"award-number":["S2021-YF-YBSF-0094"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114703","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T12:16:23Z","timestamp":1775823383000},"page":"114703","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Enhancing single image compressive sensing via pyramid-based multi-scale sampling"],"prefix":"10.1016","volume":"176","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-5462","authenticated-orcid":false,"given":"Huake","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingsong","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jutao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114703_b1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R., 2017. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops. CVPR Workshops, pp. 1122\u20131131.","DOI":"10.1109\/CVPRW.2017.150"},{"issue":"4","key":"10.1016\/j.engappai.2026.114703_b2","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1109\/TIT.2011.2111670","article-title":"Deterministic construction of binary, bipolar, and ternary compressed sensing matrices","volume":"57","author":"Amini","year":"2011","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"1","key":"10.1016\/j.engappai.2026.114703_b3","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A fast iterative shrinkage-thresholding algorithm for linear inverse problems","volume":"2","author":"Beck","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"10.1016\/j.engappai.2026.114703_b4","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M., 2012. Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding. In: Bowden, R., Collomosse, J.P., Mikolajczyk, K. (Eds.), British Machine Vision Conference. BMVC, pp. 1\u201310.","DOI":"10.5244\/C.26.135"},{"issue":"12","key":"10.1016\/j.engappai.2026.114703_b5","doi-asserted-by":"crossref","first-page":"5406","DOI":"10.1109\/TIT.2006.885507","article-title":"Near-optimal signal recovery from random projections: Universal encoding strategies?","volume":"52","author":"Cand\u00e8s","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"10.1016\/j.engappai.2026.114703_b6","doi-asserted-by":"crossref","unstructured":"Canh, T.N., Jeon, B., 2019. Difference of convolution for deep compressive sensing. In: 2019 IEEE International Conference on Image Processing. ICIP, pp. 2105\u20132109.","DOI":"10.1109\/ICIP.2019.8803165"},{"key":"10.1016\/j.engappai.2026.114703_b7","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/TCI.2020.3034433","article-title":"Multi-scale deep compressive imaging","volume":"7","author":"Canh","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"4","key":"10.1016\/j.engappai.2026.114703_b8","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/TCSVT.2019.2898908","article-title":"Compressive sensing multi-layer residual coefficients for image coding","volume":"30","author":"Chen","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b9","doi-asserted-by":"crossref","unstructured":"Chen, W., Yang, C., Yang, X., 2022. FSOINET: feature-space optimization-inspired network for image compressive sensing. In: IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP, pp. 2460\u20132464.","DOI":"10.1109\/ICASSP43922.2022.9746648"},{"key":"10.1016\/j.engappai.2026.114703_b10","doi-asserted-by":"crossref","first-page":"5412","DOI":"10.1109\/TIP.2022.3195319","article-title":"Content-aware scalable deep compressed sensing","volume":"31","author":"Chen","year":"2022","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"10.1016\/j.engappai.2026.114703_b11","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1109\/TPAMI.2022.3161934","article-title":"Recurrent neural networks for snapshot compressive imaging","volume":"45","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114703_b12","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1109\/TMM.2021.3132489","article-title":"Image compressed sensing using non-local neural network","volume":"25","author":"Cui","year":"2023","journal-title":"IEEE Trans. Multim."},{"key":"10.1016\/j.engappai.2026.114703_b13","doi-asserted-by":"crossref","unstructured":"Cui, W., Liu, S., Zhao, D., 2022. Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 2739\u20132748.","DOI":"10.1145\/3503161.3548389"},{"key":"10.1016\/j.engappai.2026.114703_b14","doi-asserted-by":"crossref","unstructured":"Cui, W., Xu, H., Gao, X., Zhang, S., Jiang, F., Zhao, D., 2018. An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios. In: IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP, pp. 1748\u20131752.","DOI":"10.1109\/ICASSP.2018.8461766"},{"issue":"1","key":"10.1016\/j.engappai.2026.114703_b15","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/TSP.2011.2170977","article-title":"Fast and efficient compressive sensing using structurally random matrices","volume":"60","author":"Do","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"issue":"8","key":"10.1016\/j.engappai.2026.114703_b16","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1109\/TIP.2014.2329449","article-title":"Compressive sensing via nonlocal low-rank regularization","volume":"23","author":"Dong","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114703_b17","doi-asserted-by":"crossref","unstructured":"Fan, Z.-E., Lian, F., Quan, J.-N., 2022. Global Sensing and Measurements Reuse for Image Compressed Sensing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 8954\u20138963.","DOI":"10.1109\/CVPR52688.2022.00875"},{"key":"10.1016\/j.engappai.2026.114703_b18","unstructured":"Fowler, J.E., Mun, S., Tramel, E.W., 2011. Multiscale block compressed sensing with smoothed projected landweber reconstruction. In: 19th European Signal Processing Conference. pp. 564\u2013568."},{"key":"10.1016\/j.engappai.2026.114703_b19","doi-asserted-by":"crossref","unstructured":"Gan, L., 2007. Block compressed sensing of natural images. In: 15th International Conference on Digital Signal Processing. pp. 403\u2013406.","DOI":"10.1109\/ICDSP.2007.4288604"},{"issue":"4","key":"10.1016\/j.engappai.2026.114703_b20","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1109\/TCYB.2021.3127657","article-title":"AutoBCS: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction","volume":"53","author":"Gan","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.114703_b21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TCI.2023.3244396","article-title":"From patch to pixel: A transformer-based hierarchical framework for compressive image sensing","volume":"9","author":"Gan","year":"2023","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"5","key":"10.1016\/j.engappai.2026.114703_b22","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1109\/TCSVT.2023.3325340","article-title":"Learned two-step iterative shrinkage thresholding algorithm for deep compressive sensing","volume":"34","author":"Gan","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b23","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.image.2018.06.004","article-title":"Construction of efficient and structural chaotic sensing matrix for compressive sensing","volume":"68","author":"Gan","year":"2018","journal-title":"Signal Process., Image Commun."},{"key":"10.1016\/j.engappai.2026.114703_b24","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.engappai.2026.114703_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2021.107998","article-title":"Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing","volume":"183","author":"Hua","year":"2021","journal-title":"Signal Process."},{"key":"10.1016\/j.engappai.2026.114703_b26","series-title":"IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Multi-channel adaptive partitioning network for block-based image compressive sensing","author":"Hui","year":"2022"},{"key":"10.1016\/j.engappai.2026.114703_b27","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\/CVF Conference on Computer Vision and Pattern Recognition. pp. 449\u2013458.","DOI":"10.1109\/CVPR.2016.55"},{"issue":"3","key":"10.1016\/j.engappai.2026.114703_b28","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s10589-013-9576-1","article-title":"An efficient augmented Lagrangian method with applications to total variation minimization","volume":"56","author":"Li","year":"2013","journal-title":"Comput. Optim. Appl."},{"key":"10.1016\/j.engappai.2026.114703_b29","doi-asserted-by":"crossref","unstructured":"Liu, R., Li, S., Hou, C., 2019. An end-to-end multi-scale residual reconstruction network for image compressive sensing. In: 2019 IEEE International Conference on Image Processing. ICIP, pp. 2070\u20132074.","DOI":"10.1109\/ICIP.2019.8803190"},{"key":"10.1016\/j.engappai.2026.114703_b30","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"},{"issue":"1","key":"10.1016\/j.engappai.2026.114703_b31","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/TSP.2017.2757915","article-title":"Binary matrices for compressed sensing","volume":"66","author":"Lu","year":"2018","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.engappai.2026.114703_b32","doi-asserted-by":"crossref","unstructured":"Martin, D.R., Fowlkes, C.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\u2013425.","DOI":"10.1109\/ICCV.2001.937655"},{"key":"10.1016\/j.engappai.2026.114703_b33","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":"10.1016\/j.engappai.2026.114703_b34","doi-asserted-by":"crossref","unstructured":"Mun, S., Fowler, J.E., 2009. Block compressed sensing of images using directional transforms. In: IEEE International Conference on Image Processing. ICIP, pp. 3021\u20133024.","DOI":"10.1109\/ICIP.2009.5414429"},{"key":"10.1016\/j.engappai.2026.114703_b35","doi-asserted-by":"crossref","unstructured":"Rick Chang, J., Li, C.-L., Poczos, B., Vijaya Kumar, B., Sankaranarayanan, A.C., 2017. One network to solve them all\u2013solving linear inverse problems using deep projection models. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 5888\u20135897.","DOI":"10.1109\/ICCV.2017.627"},{"issue":"4","key":"10.1016\/j.engappai.2026.114703_b36","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1137\/08072975X","article-title":"Compressive sensing by random convolution","volume":"2","author":"Romberg","year":"2009","journal-title":"SIAM J. Imaging Sci."},{"key":"10.1016\/j.engappai.2026.114703_b37","doi-asserted-by":"crossref","first-page":"6991","DOI":"10.1109\/TIP.2022.3217365","article-title":"TransCS: A transformer-based hybrid architecture for image compressed sensing","volume":"31","author":"Shen","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114703_b38","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z., 2016. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 1874\u20131883.","DOI":"10.1109\/CVPR.2016.207"},{"key":"10.1016\/j.engappai.2026.114703_b39","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":"10.1016\/j.engappai.2026.114703_b40","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/TIP.2019.2928136","article-title":"Image compressed sensing using convolutional neural network","volume":"29","author":"Shi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"2","key":"10.1016\/j.engappai.2026.114703_b41","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TCSVT.2020.2978703","article-title":"Video compressed sensing using a convolutional neural network","volume":"31","author":"Shi","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b42","doi-asserted-by":"crossref","unstructured":"Song, J., Chen, B., Zhang, J., 2021. Memory-augmented deep unfolding network for compressive sensing. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 4249\u20134258.","DOI":"10.1145\/3474085.3475562"},{"key":"10.1016\/j.engappai.2026.114703_b43","doi-asserted-by":"crossref","first-page":"9482","DOI":"10.1109\/TIP.2020.3023629","article-title":"Dual-path attention network for compressed sensing image reconstruction","volume":"29","author":"Sun","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.engappai.2026.114703_b44","doi-asserted-by":"crossref","first-page":"4655","DOI":"10.1109\/TIT.2007.909108","article-title":"Signal recovery from random measurements via orthogonal matching pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE Trans. Inf. Theory"},{"key":"10.1016\/j.engappai.2026.114703_b45","doi-asserted-by":"crossref","first-page":"5709","DOI":"10.1109\/TMM.2025.3543015","article-title":"Multi-scale retinex unfolding network for low-light image enhancement","volume":"27","author":"Wang","year":"2025","journal-title":"IEEE Trans. Multim."},{"key":"10.1016\/j.engappai.2026.114703_b46","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1109\/TIP.2023.3274967","article-title":"Versatile denoising-based approximate message passing for compressive sensing","volume":"32","author":"Wang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114703_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111958","article-title":"Division gets better: Learning brightness-aware and detail-sensitive representations for low-light image enhancement","volume":"299","author":"Wang","year":"2024","journal-title":"Knowl.-Based Syst."},{"issue":"2","key":"10.1016\/j.engappai.2026.114703_b48","doi-asserted-by":"crossref","first-page":"1700","DOI":"10.1109\/TCSVT.2024.3480930","article-title":"Extracting noise and darkness: Low-light image enhancement via dual prior guidance","volume":"35","author":"Wang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b49","first-page":"1","article-title":"A wavelet-domain consistency-constrained compressive sensing framework based on memory-boosted guidance filtering","volume":"73","author":"Wang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114703_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109099","article-title":"Dual-domain sampling and feature-domain optimization network for image compressive sensing","volume":"137","author":"Xiang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114703_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111659","article-title":"Image compressed sensing: From deep learning to adaptive learning","volume":"293","author":"Xie","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114703_b52","doi-asserted-by":"crossref","unstructured":"Xu, K., Zhang, Z., Ren, F., 2018. Lapran: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 485\u2013500.","DOI":"10.1007\/978-3-030-01249-6_30"},{"issue":"3","key":"10.1016\/j.engappai.2026.114703_b53","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/TPAMI.2018.2883941","article-title":"ADMM-CSNet: A deep learning approach for image compressive sensing","volume":"42","author":"Yang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.engappai.2026.114703_b54","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TIP.2023.3274988","article-title":"CSformer: Bridging convolution and transformer for compressive sensing","volume":"32","author":"Ye","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114703_b55","doi-asserted-by":"crossref","unstructured":"Yiasemis, G., Sonke, J.-J., S\u00e1nchez, C., Teuwen, J., 2022. Recurrent variational network: a deep learning inverse problem Solver applied to the task of accelerated MRI reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 732\u2013741.","DOI":"10.1109\/CVPR52688.2022.00081"},{"key":"10.1016\/j.engappai.2026.114703_b56","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108758","article-title":"Multilevel wavelet-based hierarchical networks for image compressed sensing","volume":"129","author":"Yin","year":"2022","journal-title":"Pattern Recognit."},{"issue":"10","key":"10.1016\/j.engappai.2026.114703_b57","doi-asserted-by":"crossref","first-page":"9562","DOI":"10.1109\/TCSVT.2024.3399764","article-title":"Dual-domain feature fusion and multi-level memory-enhanced network for spectral compressive imaging","volume":"34","author":"Ying","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b58","doi-asserted-by":"crossref","first-page":"6066","DOI":"10.1109\/TIP.2021.3091834","article-title":"COAST: controllable arbitrary-sampling NeTwork for compressive sensing","volume":"30","author":"You","year":"2021","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"10.1016\/j.engappai.2026.114703_b59","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.1109\/TMM.2020.2967646","article-title":"Image compression based on compressive sensing: End-to-end comparison with JPEG","volume":"22","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Multim."},{"key":"10.1016\/j.engappai.2026.114703_b60","series-title":"Curves and Surfaces","first-page":"711","article-title":"On single image scale-up using sparse-representations","volume":"vol. 6920","author":"Zeyde","year":"2010"},{"issue":"1","key":"10.1016\/j.engappai.2026.114703_b61","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MSP.2022.3217936","article-title":"Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling","volume":"40","author":"Zha","year":"2023","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.engappai.2026.114703_b62","doi-asserted-by":"crossref","first-page":"8960","DOI":"10.1109\/TIP.2020.3021291","article-title":"Group sparsity residual constraint with non-local priors for image restoration","volume":"29","author":"Zha","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"10.1016\/j.engappai.2026.114703_b63","doi-asserted-by":"crossref","first-page":"10817","DOI":"10.1109\/TCSVT.2024.3409421","article-title":"Progressive content-aware coded hyperspectral snapshot compressive imaging","volume":"34","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b64","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\/CVF Conference on Computer Vision and Pattern Recognition. pp. 1828\u20131837.","DOI":"10.1109\/CVPR.2018.00196"},{"key":"10.1016\/j.engappai.2026.114703_b65","doi-asserted-by":"crossref","first-page":"5676","DOI":"10.1109\/TMM.2022.3198323","article-title":"AMS-Net: Adaptive multi-scale network for image compressive sensing","volume":"25","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Multim."},{"issue":"11","key":"10.1016\/j.engappai.2026.114703_b66","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1109\/TCSVT.2018.2879983","article-title":"Fast parallel implementation of dual-camera compressive hyperspectral imaging system","volume":"29","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.114703_b67","series-title":"Practical blind denoising via swin-conv-unet and data synthesis","author":"Zhang","year":"2022"},{"key":"10.1016\/j.engappai.2026.114703_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110963","article-title":"Optimization-inspired cumulative transmission network for image compressive sensing","volume":"279","author":"Zhang","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114703_b69","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1109\/TIP.2020.3044472","article-title":"AMP-Net: Denoising-based deep unfolding for compressive image sensing","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"issue":"7","key":"10.1016\/j.engappai.2026.114703_b70","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.1109\/TPAMI.2020.2968521","article-title":"Residual dense network for image restoration","volume":"43","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"10.1016\/j.engappai.2026.114703_b71","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1109\/TIP.2014.2323127","article-title":"Group-based sparse representation for image restoration","volume":"23","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"10.1016\/j.engappai.2026.114703_b72","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/JSTSP.2020.2977507","article-title":"Optimization-inspired compact deep compressive sensing","volume":"14","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"10.1016\/j.engappai.2026.114703_b73","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110681","article-title":"Boundary-constrained interpretable image reconstruction network for deep compressive sensing","volume":"275","author":"Zhao","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114703_b74","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112022","article-title":"A survey on image compressive sensing: From classical theory to the latest explicable deep learning","volume":"170","author":"Zhao","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114703_b75","doi-asserted-by":"crossref","first-page":"2022","DOI":"10.1109\/TMM.2022.3142952","article-title":"Recognition-oriented image compressive sensing with deep learning","volume":"25","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Multim."},{"key":"10.1016\/j.engappai.2026.114703_b76","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1109\/TMM.2020.3014561","article-title":"Multi-channel deep networks for block-based image compressive sensing","volume":"23","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Multim."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626009851?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626009851?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:13:20Z","timestamp":1778778800000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626009851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":76,"alternative-id":["S0952197626009851"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114703","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing single image compressive sensing via pyramid-based multi-scale sampling","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114703","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114703"}}