{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:09:15Z","timestamp":1777604955527,"version":"3.51.4"},"reference-count":85,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.knosys.2025.114637","type":"journal-article","created":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:13:03Z","timestamp":1760220783000},"page":"114637","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"PB","title":["High-rank corrected multi-head self attention for image super resolution"],"prefix":"10.1016","volume":"330","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1123-5422","authenticated-orcid":false,"given":"Ying","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Zihao","family":"Ren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9334-0659","authenticated-orcid":false,"given":"Yajun","family":"Qiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-888X","authenticated-orcid":false,"given":"Bin","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Shihao","family":"Kou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8546-2129","authenticated-orcid":false,"given":"Caiwen","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Tianliang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.knosys.2025.114637_bib0001","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1109\/TGRS.2019.2959020","article-title":"Coupled Adversarial Training for Remote Sensing Image Super-Resolution","volume":"58","author":"Lei","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2025.114637_bib0002","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"9168","article-title":"Towards Real-World Blind Face Restoration With Generative Facial Prior","author":"Wang","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0003","series-title":"Proceedings of the IEEE\/CVF winter conference on applications of computer vision","first-page":"2195","article-title":"Multimodal multi-head convolutional attention with various kernel sizes for medical image super-resolution","author":"Georgescu","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0004","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII 16","first-page":"645","article-title":"Video super-resolution with recurrent structure-detail network","author":"Isobe","year":"2020"},{"issue":"3","key":"10.1016\/j.knosys.2025.114637_bib0005","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/TCSVT.2023.3297673","article-title":"Dual Circle Contrastive Learning-Based Blind Image Super-Resolution","volume":"34","author":"Qiu","year":"2024","journal-title":"IEEE Trans. Circuits Systems Video Technol."},{"key":"10.1016\/j.knosys.2025.114637_bib0006","article-title":"CTE-Net: Contextual Texture Enhancement Network for Image Super-Resolution","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.knosys.2025.114637_bib0007","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TMM.2021.3134172","article-title":"A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution","volume":"25","author":"Park","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2025.114637_bib0008","series-title":"Computer Vision - ECCV 2014","first-page":"184","article-title":"Learning a Deep Convolutional Network for Image Super-Resolution","author":"Dong","year":"2014"},{"key":"10.1016\/j.knosys.2025.114637_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107798","article-title":"Image super-resolution via channel attention and spatial graph convolutional network","volume":"112","author":"Yang","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.knosys.2025.114637_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110718","article-title":"Multi-scale information distillation network for efficient image super-resolution","volume":"275","author":"Hu","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.knosys.2025.114637_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109376","article-title":"LKASR: Large kernel attention for lightweight image super-resolution","volume":"252","author":"Feng","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.knosys.2025.114637_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108984","article-title":"Weakly-supervised contrastive learning-based implicit degradation modeling for blind image super-resolution","volume":"249","author":"Zhang","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2025.114637_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106663","article-title":"Kernel-attended residual network for single image super-resolution","volume":"213","author":"Dun","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2025.114637_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107520","article-title":"Hierarchical accumulation network with grid attention for image super-resolution","volume":"233","author":"Yang","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2025.114637_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112274","article-title":"Asymmetric convolutional modulation network for efficient image super-resolution","volume":"301","author":"Xie","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2025.114637_bib0016","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TMM.2023.3272474","article-title":"Cross-Receptive Focused Inference Network for Lightweight Image Super-Resolution","volume":"26","author":"Li","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2025.114637_bib0017","series-title":"Advances in Neural Information Processing Systems","article-title":"Attention is All you Need","volume":"30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0018","series-title":"International Conference on Learning Representations","article-title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","author":"Dosovitskiy","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0019","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"10012","article-title":"Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0020","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"12009","article-title":"Swin Transformer V2: Scaling Up Capacity and Resolution","author":"Liu","year":"2022"},{"key":"10.1016\/j.knosys.2025.114637_bib0021","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops","first-page":"1833","article-title":"SwinIR: Image Restoration Using Swin Transformer","author":"Liang","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0022","series-title":"European conference on computer vision","first-page":"649","article-title":"Efficient long-range attention network for image super-resolution","author":"Zhang","year":"2022"},{"key":"10.1016\/j.knosys.2025.114637_bib0023","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"12312","article-title":"Dual Aggregation Transformer for Image Super-Resolution","author":"Chen","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0024","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6180","article-title":"WaveFormer: wavelet transformer for noise-robust video inpainting","volume":"38","author":"Wu","year":"2024"},{"key":"10.1016\/j.knosys.2025.114637_bib0025","unstructured":"Z. Wu, K. Chen, K. Li, H. Fan, Y. Yang, BVINet: Unlocking blind video inpainting with zero annotations, arXiv: 2502.01181(2025)."},{"key":"10.1016\/j.knosys.2025.114637_bib0026","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"12780","article-title":"SRFormer: Permuted Self-Attention for Single Image Super-Resolution","author":"Zhou","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0027","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"18278","article-title":"Efficient and Explicit Modelling of Image Hierarchies for Image Restoration","author":"Li","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0028","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"26120","article-title":"CFAT: Unleashing Triangular Windows for Image Super-resolution","author":"Ray","year":"2024"},{"key":"10.1016\/j.knosys.2025.114637_bib0029","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"22367","article-title":"Activating More Pixels in Image Super-Resolution Transformer","author":"Chen","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0030","unstructured":"D. Zhang, F. Huang, S. Liu, X. Wang, Z. Jin, SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution, 2023. arXiv: 2208.11247."},{"key":"10.1016\/j.knosys.2025.114637_bib0031","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"12514","article-title":"Feature Modulation Transformer: Cross-Refinement of Global Representation via High-Frequency Prior for Image Super-Resolution","author":"Li","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0032","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"12792","article-title":"DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution","author":"Li","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0033","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"22378","article-title":"Omni Aggregation Networks for Lightweight Image Super-Resolution","author":"Wang","year":"2023"},{"issue":"2","key":"10.1016\/j.knosys.2025.114637_bib0034","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1109\/TPAMI.2022.3167175","article-title":"Learning Enriched Features for Fast Image Restoration and Enhancement","volume":"45","author":"Zamir","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2025.114637_bib0035","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","article-title":"Real Image Denoising With Feature Attention","author":"Anwar","year":"2019"},{"key":"10.1016\/j.knosys.2025.114637_bib0036","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","article-title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","author":"Zhang","year":"2018"},{"issue":"2","key":"10.1016\/j.knosys.2025.114637_bib0037","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/38.988747","article-title":"Example-based super-resolution","volume":"22","author":"Freeman","year":"2002","journal-title":"IEEE Comput. Graph. Appl."},{"key":"10.1016\/j.knosys.2025.114637_bib0038","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","author":"Kim","year":"2016"},{"key":"10.1016\/j.knosys.2025.114637_bib0039","series-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)","article-title":"MemNet: A Persistent Memory Network for Image Restoration","author":"Tai","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0040","series-title":"Proceedings of the European conference on computer vision (ECCV)","first-page":"252","article-title":"Fast, accurate, and lightweight super-resolution with cascading residual network","author":"Ahn","year":"2018"},{"key":"10.1016\/j.knosys.2025.114637_bib0041","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Residual Dense Network for Image Super-Resolution","author":"Zhang","year":"2018"},{"key":"10.1016\/j.knosys.2025.114637_bib0042","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Deeply-Recursive Convolutional Network for Image Super-Resolution","author":"Kim","year":"2016"},{"key":"10.1016\/j.knosys.2025.114637_bib0043","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Image Super-Resolution via Deep Recursive Residual Network","author":"Tai","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0044","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1874","article-title":"Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","author":"Shi","year":"2016"},{"key":"10.1016\/j.knosys.2025.114637_bib0045","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution","author":"Lai","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0046","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXV 16","first-page":"492","article-title":"Learning enriched features for real image restoration and enhancement","author":"Zamir","year":"2020"},{"key":"10.1016\/j.knosys.2025.114637_bib0047","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Second-Order Attention Network for Single Image Super-Resolution","author":"Dai","year":"2019"},{"key":"10.1016\/j.knosys.2025.114637_bib0048","series-title":"International Conference on Learning Representations","article-title":"Residual Non-local Attention Networks for Image Restoration","author":"Zhang","year":"2019"},{"key":"10.1016\/j.knosys.2025.114637_bib0049","series-title":"Advances in Neural Information Processing Systems","article-title":"Non-Local Recurrent Network for Image Restoration","volume":"31","author":"Liu","year":"2018"},{"key":"10.1016\/j.knosys.2025.114637_bib0050","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XII 16","first-page":"191","article-title":"Single image super-resolution via a holistic attention network","author":"Niu","year":"2020"},{"key":"10.1016\/j.knosys.2025.114637_bib0051","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"3517","article-title":"Image Super-Resolution With Non-Local Sparse Attention","author":"Mei","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0052","series-title":"Proceedings of the 37th International Conference on Machine Learning","first-page":"864","article-title":"Low-Rank Bottleneck in Multi-head Attention Models","volume":"119","author":"Bhojanapalli","year":"2020"},{"key":"10.1016\/j.knosys.2025.114637_bib0053","series-title":"International Conference on Learning Representations","article-title":"On Identifiability in Transformers","author":"Brunner","year":"2020"},{"key":"10.1016\/j.knosys.2025.114637_bib0054","series-title":"International Conference on Learning Representations","article-title":"Rethinking Attention with Performers","author":"Choromanski","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0055","series-title":"Advances in Neural Information Processing Systems","first-page":"17723","article-title":"Long-Short Transformer: Efficient Transformers for Language and Vision","volume":"34","author":"Zhu","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0056","unstructured":"S. Wang, B.Z. Li, M. Khabsa, H. Fang, H. Ma, Linformer: Self-attention with linear complexity, arXiv: 2006.04768(2020)."},{"key":"10.1016\/j.knosys.2025.114637_bib0057","series-title":"2023 IEEE International Symposium on High-Performance Computer Architecture (HPCA)","first-page":"415","article-title":"Vitality: Unifying low-rank and sparse approximation for vision transformer acceleration with a linear taylor attention","author":"Dass","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0058","unstructured":"D. Xiao, Q. Meng, S. Li, X. Yuan, Improving Transformers with Dynamically Composable Multi-Head Attention, arXiv: 2405.08553(2024)."},{"key":"10.1016\/j.knosys.2025.114637_bib0059","unstructured":"H. Kang, M.-H. Yang, J. Ryu, Interactive Multi-Head Self-Attention with Linear Complexity, arXiv: 2402.17507(2024)."},{"key":"10.1016\/j.knosys.2025.114637_bib0060","unstructured":"Z. Ilyas, N. Akhtar, D. Suter, S.Z. Gilani, GLMHA A Guided Low-rank Multi-Head Self-Attention for Efficient Image Restoration and Spectral Reconstruction, arXiv: 2410.00380(2024)."},{"key":"10.1016\/j.knosys.2025.114637_bib0061","series-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","first-page":"5797","article-title":"Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned","author":"Voita","year":"2019"},{"key":"10.1016\/j.knosys.2025.114637_bib0062","unstructured":"J.L. Ba, J.R. Kiros, G.E. Hinton, Layer normalization, arXiv: 1607.06450(2016)."},{"key":"10.1016\/j.knosys.2025.114637_bib0063","unstructured":"D. Hendrycks, K. Gimpel, Gaussian error linear units (gelus), arXiv: 1606.08415(2016)."},{"key":"10.1016\/j.knosys.2025.114637_bib0064","series-title":"Proc. ICML","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"30","author":"Maas","year":"2013"},{"key":"10.1016\/j.knosys.2025.114637_bib0065","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7794","article-title":"Non-local neural networks","author":"Wang","year":"2018"},{"key":"10.1016\/j.knosys.2025.114637_bib0066","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"9199","article-title":"Interpreting Super-Resolution Networks With Local Attribution Maps","author":"Gu","year":"2021"},{"key":"10.1016\/j.knosys.2025.114637_bib0067","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","article-title":"NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results","author":"Timofte","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0068","series-title":"Proceedings of the British Machine Vision Conference (BMVC)","first-page":"135.1","article-title":"Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding","author":"Bevilacqua","year":"2012"},{"key":"10.1016\/j.knosys.2025.114637_bib0069","series-title":"Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7","first-page":"711","article-title":"On single image scale-up using sparse-representations","author":"Zeyde","year":"2012"},{"key":"10.1016\/j.knosys.2025.114637_bib0070","series-title":"Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001","first-page":"416","article-title":"A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics","volume":"2","author":"Martin","year":"2001"},{"key":"10.1016\/j.knosys.2025.114637_bib0071","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"5197","article-title":"Single image super-resolution from transformed self-exemplars","author":"Huang","year":"2015"},{"key":"10.1016\/j.knosys.2025.114637_bib0072","doi-asserted-by":"crossref","first-page":"21811","DOI":"10.1007\/s11042-016-4020-z","article-title":"Sketch-based manga retrieval using manga109 dataset","volume":"76","author":"Matsui","year":"2017","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.knosys.2025.114637_bib0073","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3929","article-title":"Learning deep CNN denoiser prior for image restoration","author":"Zhang","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0074","unstructured":"D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv: 1412.6980(2014)."},{"key":"10.1016\/j.knosys.2025.114637_bib0075","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","article-title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","author":"Lim","year":"2017"},{"key":"10.1016\/j.knosys.2025.114637_bib0076","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3262","article-title":"Learning a single convolutional super-resolution network for multiple degradations","author":"Zhang","year":"2018"},{"key":"10.1016\/j.knosys.2025.114637_bib0077","series-title":"Proceedings of the 27th acm international conference on multimedia","first-page":"2024","article-title":"Lightweight image super-resolution with information multi-distillation network","author":"Hui","year":"2019"},{"key":"10.1016\/j.knosys.2025.114637_bib0078","series-title":"Lattice Network for Lightweight Image Restoration","first-page":"4826","volume":"45","author":"Luo","year":"2023"},{"key":"10.1016\/j.knosys.2025.114637_bib0079","unstructured":"B. Yang, G. Wu, MaxSR: Image Super-Resolution Using Improved MaxViT, arXiv: 2307.07240(2023)."},{"key":"10.1016\/j.knosys.2025.114637_bib0080","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"24108","article-title":"Image Processing GNN: Breaking Rigidity in Super-Resolution","author":"Tian","year":"2024"},{"key":"10.1016\/j.knosys.2025.114637_bib0081","series-title":"Proceedings of the 41st International Conference on Machine Learning","first-page":"58158","article-title":"See More Details: Efficient Image Super-Resolution by Experts Mining","volume":"235","author":"Zamfir","year":"2024"},{"key":"10.1016\/j.knosys.2025.114637_bib0082","first-page":"1","article-title":"Efficient Dual-Branch Information Interaction Network for Lightweight Image Super-Resolution","volume":"73","author":"Jin","year":"2024","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.1016\/j.knosys.2025.114637_bib0083","unstructured":"W. Li, X. Lu, J. Lu, X. Zhang, J. Jia, On efficient transformer and image pre-training for low-level vision, arXiv: 2112.10175 3 (7) (2021) 8."},{"key":"10.1016\/j.knosys.2025.114637_bib0084","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","author":"Zhang","year":"2018"},{"key":"10.1016\/j.knosys.2025.114637_bib0085","series-title":"Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)","article-title":"Lightweight bimodal network for single-image super-resolution via symmetric cnn and recursive transformer","author":"Gao","year":"2022"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705125016764?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705125016764?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T03:02:33Z","timestamp":1765594953000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705125016764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":85,"alternative-id":["S0950705125016764"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2025.114637","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"High-rank corrected multi-head self attention for image super resolution","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2025.114637","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114637"}}