{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:16:43Z","timestamp":1783455403362,"version":"3.55.0"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"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":["62475199"],"award-info":[{"award-number":["62475199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473297"],"award-info":[{"award-number":["62473297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23B2050"],"award-info":[{"award-number":["U23B2050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114330","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:21:49Z","timestamp":1782850909000},"page":"114330","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["Light field image blind super-resolution via degradation representation learning"],"prefix":"10.1016","volume":"180","author":[{"given":"Kailing","family":"Yong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7507-1810","authenticated-orcid":false,"given":"Fan","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"You","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haonan","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"11","key":"10.1016\/j.patcog.2026.114330_b1","first-page":"15660","article-title":"Beyond photometric consistency: Geometry-based occlusion-aware unsupervised light field disparity estimation","volume":"35","author":"Zhou","year":"2023","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.patcog.2026.114330_b2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/LSP.2020.3043990","article-title":"Multi-volumetric refocusing of light fields","volume":"28","author":"Jayaweera","year":"2020","journal-title":"IEEE Signal Processing Letters"},{"key":"10.1016\/j.patcog.2026.114330_b3","series-title":"IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Accurate 3D reconstruction from circular light field using CNN-LSTM","author":"Song","year":"2020"},{"issue":"3","key":"10.1016\/j.patcog.2026.114330_b4","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/TPAMI.2019.2945027","article-title":"High-dimensional dense residual convolutional neural network for light field reconstruction","volume":"43","author":"Meng","year":"2019","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"10.1016\/j.patcog.2026.114330_b5","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TPAMI.2022.3152488","article-title":"Disentangling light fields for super-resolution and disparity estimation","volume":"45","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.patcog.2026.114330_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111616","article-title":"Cascade residual learning based adaptive feature aggregation for light field super-resolution","volume":"165","author":"Zhang","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.patcog.2026.114330_b7","doi-asserted-by":"crossref","unstructured":"Z. Liang, Y. Wang, L. Wang, J. Yang, S. Zhou, Y. Guo, Learning non-local spatial-angular correlation for light field image super-resolution, in: IEEE\/CVF International Conference on Computer Vision, 2023, pp. 12376\u201312386.","DOI":"10.1109\/ICCV51070.2023.01137"},{"key":"10.1016\/j.patcog.2026.114330_b8","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1109\/TMM.2023.3282465","article-title":"Exploiting spatial and angular correlations with deep efficient transformers for light field image super-resolution","volume":"26","author":"Cong","year":"2023","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.patcog.2026.114330_b9","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1109\/TMM.2024.3521795","article-title":"Beyond subspace isolation: many-to-many transformer for light field image super-resolution","volume":"27","author":"Hu","year":"2025","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.patcog.2026.114330_b10","article-title":"Multi-level disparity-guided transformers for light field spatial super-resolution","author":"Liao","year":"2025","journal-title":"Pattern Recognition"},{"issue":"3","key":"10.1016\/j.patcog.2026.114330_b11","doi-asserted-by":"crossref","first-page":"5559","DOI":"10.1109\/TNNLS.2024.3378420","article-title":"Real-world light field image super-resolution via degradation modulation","volume":"36","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.patcog.2026.114330_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2025.104295","article-title":"Incorporating degradation estimation in light field spatial super-resolution","volume":"252","author":"Xiao","year":"2025","journal-title":"Computer Vision and Image Understanding"},{"issue":"9","key":"10.1016\/j.patcog.2026.114330_b13","doi-asserted-by":"crossref","first-page":"4274","DOI":"10.1109\/TIP.2018.2834819","article-title":"LFNet: A novel bidirectional recurrent convolutional neural network for light-field image super-resolution","volume":"27","author":"Wang","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.patcog.2026.114330_b14","doi-asserted-by":"crossref","unstructured":"S. Zhang, Y. Lin, H. Sheng, Residual networks for light field image super-resolution, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11046\u201311055.","DOI":"10.1109\/CVPR42600.2020.01106"},{"key":"10.1016\/j.patcog.2026.114330_b15","doi-asserted-by":"crossref","unstructured":"J. Jin, J. Hou, J. Chen, S. Kwong, Light field spatial super-resolution via deep combinatorial geometry embedding and structural consistency regularization, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2260\u20132269.","DOI":"10.1109\/CVPR42600.2020.00233"},{"key":"10.1016\/j.patcog.2026.114330_b16","series-title":"European Conference on Computer Vision","first-page":"290","article-title":"Spatial-angular interaction for light field image super-resolution","author":"Wang","year":"2020"},{"key":"10.1016\/j.patcog.2026.114330_b17","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TMM.2021.3124385","article-title":"Intra-inter view interaction network for light field image super-resolution","volume":"25","author":"Liu","year":"2021","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.patcog.2026.114330_b18","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/LSP.2022.3146798","article-title":"Light field image super-resolution with transformers","volume":"29","author":"Liang","year":"2022","journal-title":"IEEE Signal Processing Letters"},{"key":"10.1016\/j.patcog.2026.114330_b19","series-title":"AAAI Conference on Artificial Intelligence","first-page":"8700","article-title":"Occlusion-embedded hybrid transformer for light field super-resolution","volume":"vol. 39","author":"Xiao","year":"2025"},{"issue":"2","key":"10.1016\/j.patcog.2026.114330_b20","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TCSVT.2025.3612939","article-title":"Learning implicit and detail-enhanced network for light field image spatial-angular super-resolution","volume":"36","author":"Liu","year":"2026","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.patcog.2026.114330_b21","doi-asserted-by":"crossref","unstructured":"S.Y. Kim, H. Sim, M. Kim, Koalanet: Blind super-resolution using kernel-oriented adaptive local adjustment, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10611\u201310620.","DOI":"10.1109\/CVPR46437.2021.01047"},{"key":"10.1016\/j.patcog.2026.114330_b22","doi-asserted-by":"crossref","unstructured":"R. Zhou, S. Susstrunk, Kernel modeling super-resolution on real low-resolution images, in: IEEE\/CVF International Conference on Computer Vision, 2019, pp. 2433\u20132443.","DOI":"10.1109\/ICCV.2019.00252"},{"key":"10.1016\/j.patcog.2026.114330_b23","doi-asserted-by":"crossref","unstructured":"K. Zhang, J. Liang, L. Van Gool, R. Timofte, Designing a practical degradation model for deep blind image super-resolution, in: IEEE\/CVF International Conference on Computer Vision, 2021, pp. 4791\u20134800.","DOI":"10.1109\/ICCV48922.2021.00477"},{"key":"10.1016\/j.patcog.2026.114330_b24","doi-asserted-by":"crossref","unstructured":"Z. Yang, J. Xia, S. Li, X. Huang, S. Zhang, Z. Liu, Y. Fu, Y. Liu, A dynamic kernel prior model for unsupervised blind image super-resolution, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 26046\u201326056.","DOI":"10.1109\/CVPR52733.2024.02461"},{"key":"10.1016\/j.patcog.2026.114330_b25","doi-asserted-by":"crossref","unstructured":"L. Wang, Y. Wang, X. Dong, Q. Xu, J. Yang, W. An, Y. Guo, Unsupervised degradation representation learning for blind super-resolution, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10581\u201310590.","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"10.1016\/j.patcog.2026.114330_b26","series-title":"European Conference on Computer Vision","first-page":"574","article-title":"Efficient and degradation-adaptive network for real-world image super-resolution","author":"Liang","year":"2022"},{"key":"10.1016\/j.patcog.2026.114330_b27","series-title":"International Conference on Learning Representations","article-title":"Knowledge distillation based degradation estimation for blind super-resolution","author":"Xia","year":"2023"},{"key":"10.1016\/j.patcog.2026.114330_b28","doi-asserted-by":"crossref","unstructured":"Z. Yin, M. Liu, X. Li, H. Yang, L. Xiao, W. Zuo, Metaf2n: Blind image super-resolution by learning efficient model adaptation from faces. In 2023 IEEE, in: IEEE\/CVF International Conference on Computer Vision, ICCV, 2023, pp. 12987\u201312998.","DOI":"10.1109\/ICCV51070.2023.01198"},{"key":"10.1016\/j.patcog.2026.114330_b29","doi-asserted-by":"crossref","first-page":"4556","DOI":"10.1109\/TIP.2024.3442613","article-title":"Lightweight prompt learning implicit degradation estimation network for blind super resolution","volume":"33","author":"Khan","year":"2024","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.patcog.2026.114330_b30","doi-asserted-by":"crossref","unstructured":"Q. Liu, C. Zhuang, P. Gao, J. Qin, Cdformer: When degradation prediction embraces diffusion model for blind image super-resolution, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 7455\u20137464.","DOI":"10.1109\/CVPR52733.2024.00712"},{"key":"10.1016\/j.patcog.2026.114330_b31","series-title":"AAAI Conference on Artificial Intelligence","first-page":"2177","article-title":"Unsupervised degradation representation aware transform for real-world blind image super-resolution","volume":"vol. 39","author":"Chen","year":"2025"},{"key":"10.1016\/j.patcog.2026.114330_b32","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.1109\/TIP.2025.3558442","article-title":"Content-decoupled contrastive learning-based implicit degradation modeling for blind image super-resolution","volume":"34","author":"Yuan","year":"2025","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.patcog.2026.114330_b33","doi-asserted-by":"crossref","unstructured":"J. Yuan, J. Ma, B. Wang, G. Ke, W. Hu, LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning, in: IEEE\/CVF International Conference on Computer Vision, 2025, pp. 11927\u201311936.","DOI":"10.1109\/ICCV51701.2025.01109"},{"key":"10.1016\/j.patcog.2026.114330_b34","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/978-1-4419-6533-2_12","article-title":"Bilinear interpolation","author":"Kirkland","year":"2010","journal-title":"Adv. Comput. Electron Microsc."},{"key":"10.1016\/j.patcog.2026.114330_b35","series-title":"The 2nd Canadian Conference on Computer and Robot Vision","first-page":"65","article-title":"Detection of linear and cubic interpolation in JPEG compressed images","author":"Gallagher","year":"2005"},{"issue":"4","key":"10.1016\/j.patcog.2026.114330_b36","first-page":"25","article-title":"Nearest neighbor value interpolation","volume":"3","author":"Rukundo","year":"2012","journal-title":"International Journal of Advanced Computer Science and Applications (IJACSA)"},{"issue":"2","key":"10.1016\/j.patcog.2026.114330_b37","first-page":"2","article-title":"Realesrgan: Training real-world blind super-resolution with pure synthetic data supplementary material","volume":"1","author":"Wang","year":"2022","journal-title":"Comput. Vis. Found. Open Access"},{"key":"10.1016\/j.patcog.2026.114330_b38","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1109\/TMM.2024.3521839","article-title":"Learning distinguishable degradation maps for unknown image super-resolution","volume":"27","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.patcog.2026.114330_b39","doi-asserted-by":"crossref","unstructured":"K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.patcog.2026.114330_b40","doi-asserted-by":"crossref","unstructured":"S.W. Zamir, A. Arora, S. Khan, M. Hayat, F.S. Khan, M.-H. Yang, Restormer: Efficient transformer for high-resolution image restoration, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5728\u20135739.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"10.1016\/j.patcog.2026.114330_b41","series-title":"Asian Conference on Computer Vision","first-page":"19","article-title":"A dataset and evaluation methodology for depth estimation on 4D light fields","author":"Honauer","year":"2017"},{"key":"10.1016\/j.patcog.2026.114330_b42","unstructured":"M. Rerabek, T. Ebrahimi, New light field image dataset, in: 8th International Conference on Quality of Multimedia Experience, QoMEX, 2016."},{"issue":"4","key":"10.1016\/j.patcog.2026.114330_b43","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1109\/TIP.2018.2791864","article-title":"Light field inpainting propagation via low rank matrix completion","volume":"27","author":"Le Pendu","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.patcog.2026.114330_b44","series-title":"Vision, Modeling, and Visualization","first-page":"225","article-title":"Datasets and benchmarks for densely sampled 4D light fields","volume":"vol. 13","author":"Wanner","year":"2013"},{"key":"10.1016\/j.patcog.2026.114330_b45","series-title":"The (New) Stanford Light Field Archive","author":"Vaish","year":"2008"},{"key":"10.1016\/j.patcog.2026.114330_b46","series-title":"Stanford lytro light field archive","author":"Raj","year":"2016"},{"key":"10.1016\/j.patcog.2026.114330_b47","doi-asserted-by":"crossref","unstructured":"Z. Xiao, R. Gao, Y. Liu, Y. Zhang, Z. Xiong, Toward real-world light field super-resolution, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 3408\u20133418.","DOI":"10.1109\/CVPRW59228.2023.00343"},{"key":"10.1016\/j.patcog.2026.114330_b48","unstructured":"D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, in: International Conference on Learning Representations (ICLR), 2015."},{"key":"10.1016\/j.patcog.2026.114330_b49","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/TCI.2023.3261501","article-title":"Light field image super-resolution network via joint spatial-angular and epipolar information","volume":"9","author":"Van Duong","year":"2023","journal-title":"IEEE Transactions on Computational Imaging"},{"issue":"3","key":"10.1016\/j.patcog.2026.114330_b50","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Processing Letters"},{"issue":"12","key":"10.1016\/j.patcog.2026.114330_b51","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.patcog.2026.114330_b52","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.cviu.2015.12.007","article-title":"Robust depth estimation for light field via spinning parallelogram operator","volume":"145","author":"Zhang","year":"2016","journal-title":"Computer Vision and Image Understanding"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326012951?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326012951?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:43:15Z","timestamp":1783453395000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326012951"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":52,"alternative-id":["S0031320326012951"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114330","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Light field image blind super-resolution via degradation representation learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114330","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":"114330"}}