{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:03:39Z","timestamp":1781006619785,"version":"3.54.1"},"reference-count":70,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116104","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:25:13Z","timestamp":1777875913000},"page":"116104","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Degradation classification-objective free: Learning a degradation-invariant semantic prior for all-in-one image restoration"],"prefix":"10.1016","volume":"345","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2126-2007","authenticated-orcid":false,"given":"Zihan","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6185-2964","authenticated-orcid":false,"given":"Dirui","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0344-3080","authenticated-orcid":false,"given":"Yue","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3764-2640","authenticated-orcid":false,"given":"Xiaofang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116104_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114086","article-title":"Image deraining via dual-level contextual information associated learning for autonomous driving","volume":"327","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116104_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110480","article-title":"Local and global knowledge distillation with direction-enhanced contrastive learning for single-image deraining","volume":"268","author":"Luo","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116104_b3","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3937","article-title":"Progressive image deraining networks: A better and simpler baseline","author":"Ren","year":"2019"},{"key":"10.1016\/j.knosys.2026.116104_b4","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"222","article-title":"MambaIR: A simple baseline for image restoration with state-space model","author":"Guo","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b5","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","first-page":"1833","article-title":"SwinIR: Image restoration using Swin Transformer","author":"Liang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116104_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.112998","article-title":"Reffusion: Enhancement conditional diffusion framework with dual domain interaction Transformer for image restoration","volume":"311","author":"Xie","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116104_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113579","article-title":"Towards context-aware convolutional network for image restoration","volume":"321","author":"Hao","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116104_b8","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12294","article-title":"Pre-trained image processing transformer","author":"Chen","year":"2021"},{"key":"10.1016\/j.knosys.2026.116104_b9","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1109\/TMM.2025.3535316","article-title":"All-in-one weather-degraded image restoration via adaptive degradation-aware self-prompting model","volume":"27","author":"Wen","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116104_b10","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"17632","article-title":"Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model","author":"Chen","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b11","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"111","article-title":"Restoring images in adverse weather conditions via histogram transformer","author":"Sun","year":"2025"},{"issue":"3","key":"10.1016\/j.knosys.2026.116104_b12","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TITS.2021.3117868","article-title":"Deep illumination-aware dehazing with low-light and detail enhancement","volume":"23","author":"Kim","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116104_b13","series-title":"Beyond degradation redundancy: Contrastive prompt learning for all-in-one image restoration","author":"Wu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116104_b14","series-title":"Advances in Neural Information Processing Systems","first-page":"71275","article-title":"PromptIR: Prompting for all-in-one image restoration","author":"Potlapalli","year":"2023"},{"key":"10.1016\/j.knosys.2026.116104_b15","first-page":"8386","article-title":"Debiased all-in-one image restoration with task uncertainty regularization","volume":"vol. 39, no. 8","author":"Wu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116104_b16","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1109\/TIP.2025.3566300","article-title":"Perceive-IR: Learning to perceive degradation better for all-in-one image restoration","volume":"35","author":"Zhang","year":"2026","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116104_b17","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"6442","article-title":"PromptCIR: Blind compressed image restoration with prompt learning","author":"Li","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110981","article-title":"Textual prompt guided image restoration","volume":"155","author":"Yan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116104_b19","series-title":"Towards effective multiple-in-one image restoration: A sequential and prompt learning strategy","author":"Kong","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b20","series-title":"Unified-width adaptive dynamic network for all-in-one image restoration","author":"Xu","year":"2024"},{"issue":"50","key":"10.1016\/j.knosys.2026.116104_b21","first-page":"1","article-title":"Emergence of invariance and disentanglement in deep representations","volume":"19","author":"Achille","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116104_b22","first-page":"18090","article-title":"CoPL: Contextual prompt learning for vision-language understanding","volume":"vol. 38, no. 16","author":"Goswami","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b23","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16795","article-title":"Conditional prompt learning for vision-language models","author":"Zhou","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112543","article-title":"RDM-IR: Task-adaptive deep unfolding network for all-in-one image restoration","volume":"304","author":"Cheng","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116104_b25","unstructured":"Y. Cui, S.W. Zamir, S. Khan, A. Knoll, M. Shah, F.S. Khan, AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation, in: Proceedings of the International Conference on Learning Representations, ICLR, 2025."},{"key":"10.1016\/j.knosys.2026.116104_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128959","article-title":"DRFIR: A dimensionality reduction framework for all-in-one image restoration in spatial and frequency domains","volume":"296","author":"Liu","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116104_b27","doi-asserted-by":"crossref","first-page":"3403","DOI":"10.1109\/TIP.2025.3572788","article-title":"Exploring the potential of pooling techniques for universal image restoration","volume":"34","author":"Cui","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116104_b28","doi-asserted-by":"crossref","first-page":"12496","DOI":"10.1109\/TCSVT.2024.3429557","article-title":"Omni-kernel modulation for universal image restoration","volume":"34","author":"Cui","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116104_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2024.104222","article-title":"Leveraging vision-language prompts for real-world image restoration and enhancement","volume":"250","author":"Wei","year":"2025","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.knosys.2026.116104_b30","series-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"458","article-title":"MediCLIP: Adapting CLIP for few-shot medical image anomaly detection","author":"Zhang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b31","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3941","article-title":"Rethinking prior information generation with CLIP for few-shot segmentation","author":"Wang","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b32","unstructured":"Z. Luo, F.K. Gustafsson, Z. Zhao, J. Sj\u00f6lund, T.B. Sch\u00f6n, Controlling Vision-Language Models for Multi-Task Image Restoration, in: Proceedings of the International Conference on Learning Representations, ICLR, 2024."},{"key":"10.1016\/j.knosys.2026.116104_b33","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"6641","article-title":"Photo-realistic image restoration in the wild with controlled vision-language models","author":"Luo","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b34","unstructured":"N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, J. Dean, Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, in: Proceedings of the International Conference on Learning Representations, ICLR, 2017."},{"issue":"120","key":"10.1016\/j.knosys.2026.116104_b35","first-page":"1","article-title":"Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity","volume":"23","author":"Fedus","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.knosys.2026.116104_b36","series-title":"Advances in Neural Information Processing Systems","first-page":"8583","article-title":"Scaling vision with sparse mixture of experts","author":"Riquelme","year":"2021"},{"key":"10.1016\/j.knosys.2026.116104_b37","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11999","article-title":"Swin transformer V2: Scaling up capacity and resolution","author":"Liu","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b38","series-title":"WM-MoE: Weather-aware multi-scale mixture-of-experts for blind adverse weather removal","author":"Luo","year":"2023"},{"key":"10.1016\/j.knosys.2026.116104_b39","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12753","article-title":"Complexity experts are task-discriminative learners for any image restoration","author":"Zamfir","year":"2025"},{"key":"10.1016\/j.knosys.2026.116104_b40","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"3408","article-title":"MoESR: Blind super-resolution using kernel-aware mixture of experts","author":"Emad","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b41","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: Proceedings of the International Conference on Learning Representations, ICLR, 2021."},{"key":"10.1016\/j.knosys.2026.116104_b42","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"23219","article-title":"Boosting continual learning of vision-language models via mixture-of-experts adapters","author":"Yu","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b43","unstructured":"E.J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen, LoRA: Low-Rank Adaptation of Large Language Models, in: Proceedings of the International Conference on Learning Representations, ICLR, 2022."},{"key":"10.1016\/j.knosys.2026.116104_b44","series-title":"Advances in Neural Information Processing Systems","first-page":"27682","article-title":"Leveraging sparse and shared feature activations for disentangled representation learning","author":"Fumero","year":"2023"},{"key":"10.1016\/j.knosys.2026.116104_b45","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5896","article-title":"Learning a sparse transformer network for effective image deraining","author":"Chen","year":"2023"},{"key":"10.1016\/j.knosys.2026.116104_b46","unstructured":"J. Hu, L. Jin, Z. Yao, Y. Lu, Universal Image Restoration Pre-training via Degradation Classification, in: Proceedings of the International Conference on Learning Representations, ICLR, 2025."},{"key":"10.1016\/j.knosys.2026.116104_b47","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2353","article-title":"TransWeather: Transformer-based restoration of images degraded by adverse weather conditions","author":"Valanarasu","year":"2022"},{"issue":"6","key":"10.1016\/j.knosys.2026.116104_b48","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1109\/TIP.2018.2806202","article-title":"DesnowNet: Context-aware deep network for snow removal","volume":"27","author":"Liu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116104_b49","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1633","article-title":"Heavy rain image restoration: Integrating physics model and conditional adversarial learning","author":"Li","year":"2019"},{"key":"10.1016\/j.knosys.2026.116104_b50","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2482","article-title":"Attentive generative adversarial network for raindrop removal from a single image","author":"Qian","year":"2018"},{"key":"10.1016\/j.knosys.2026.116104_b51","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"21747","article-title":"Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions","author":"Zhu","year":"2023"},{"key":"10.1016\/j.knosys.2026.116104_b52","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"17431","article-title":"All-in-one image restoration for unknown corruption","author":"Li","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b53","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"17","article-title":"Simple baselines for image restoration","author":"Chen","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b54","doi-asserted-by":"crossref","first-page":"8086","DOI":"10.1109\/TIP.2025.3638662","article-title":"M2Restore: Mixture-of-experts-based Mamba-CNN fusion framework for all-in-one image restoration","volume":"34","author":"Wang","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116104_b55","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"262","article-title":"Recurrent squeeze-and-excitation context aggregation net for single image deraining","author":"Li","year":"2018"},{"key":"10.1016\/j.knosys.2026.116104_b56","doi-asserted-by":"crossref","first-page":"7419","DOI":"10.1109\/TIP.2021.3104166","article-title":"Deep dense multi-scale network for snow removal using semantic and depth priors","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116104_b57","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14816","article-title":"Multi-stage progressive image restoration","author":"Zamir","year":"2021"},{"issue":"11","key":"10.1016\/j.knosys.2026.116104_b58","doi-asserted-by":"crossref","first-page":"12978","DOI":"10.1109\/TPAMI.2022.3183612","article-title":"Image de-raining transformer","volume":"45","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116104_b59","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"5967","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.knosys.2026.116104_b60","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5718","article-title":"Restormer: Efficient transformer for high-resolution image restoration","author":"Zamir","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b61","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3172","article-title":"All in one bad weather removal using architectural search","author":"Li","year":"2020"},{"issue":"8","key":"10.1016\/j.knosys.2026.116104_b62","doi-asserted-by":"crossref","first-page":"10346","DOI":"10.1109\/TPAMI.2023.3238179","article-title":"Restoring vision in adverse weather conditions with patch-based denoising diffusion models","volume":"45","author":"\u00d6zdenizci","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116104_b63","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"2524","article-title":"Learning diffusion texture priors for image restoration","author":"Ye","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b64","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"25445","article-title":"Selective hourglass mapping for universal image restoration based on diffusion model","author":"Zheng","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b65","first-page":"16812","article-title":"Efficient deweather mixture-of-experts with uncertainty-aware feature-wise linear modulation","volume":"vol. 38, no. 15","author":"Zhang","year":"2024"},{"issue":"10","key":"10.1016\/j.knosys.2026.116104_b66","doi-asserted-by":"crossref","first-page":"4541","DOI":"10.1007\/s11263-024-02056-0","article-title":"GridFormer: Residual dense transformer with grid structure for image restoration in adverse weather conditions","volume":"132","author":"Wang","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.knosys.2026.116104_b67","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5891","article-title":"URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement","author":"Wu","year":"2022"},{"key":"10.1016\/j.knosys.2026.116104_b68","series-title":"Proceedings of the ACM International Conference on Multimedia","first-page":"4590","article-title":"Learning structural priors via Laplacian RWKV diffusion with light-effect dataset for nighttime visibility enhancement","author":"Xie","year":"2025"},{"key":"10.1016\/j.knosys.2026.116104_b69","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"255","article-title":"OneRestore: A universal restoration framework for composite degradation","author":"Guo","year":"2024"},{"key":"10.1016\/j.knosys.2026.116104_b70","series-title":"LoRA-IR: Taming low-rank experts for efficient all-in-one image restoration","author":"Ai","year":"2024"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008300?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008300?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:30:37Z","timestamp":1781004637000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":70,"alternative-id":["S0950705126008300"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116104","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Degradation classification-objective free: Learning a degradation-invariant semantic prior for all-in-one image restoration","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116104","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 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":"116104"}}