{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T07:54:38Z","timestamp":1775548478795,"version":"3.50.1"},"reference-count":58,"publisher":"Elsevier BV","issue":"5","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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Chinese Academy of Sciences Youth Innovation Promotion Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing & Management"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ipm.2026.104708","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T23:51:43Z","timestamp":1772495503000},"page":"104708","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Progressive feature refinement network for blind gaussian image denoising"],"prefix":"10.1016","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9047-5383","authenticated-orcid":false,"given":"Sanzai","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8278-8747","authenticated-orcid":false,"given":"Lihua","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8260-6097","authenticated-orcid":false,"given":"Yi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2026.104708_bib0001","series-title":"2018\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"1692","article-title":"A high-quality denoising dataset for smartphone cameras","author":"Abdelhamed","year":"2018"},{"key":"10.1016\/j.ipm.2026.104708_bib0002","series-title":"2017\u202fIEEE Conference on computer vision and pattern recognition workshops","first-page":"1122","article-title":"Ntire 2017 challenge on single image super-resolution: Dataset and study","author":"Agustsson","year":"2017"},{"key":"10.1016\/j.ipm.2026.104708_bib0003","series-title":"2024\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"25432","article-title":"Multimodal prompt perceiver: Empower adaptiveness, generalizability and fidelity for all-in-one image restoration","author":"Ai","year":"2024"},{"issue":"3","key":"10.1016\/j.ipm.2026.104708_bib0004","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/TNNLS.2021.3131739","article-title":"Attention-based real image restoration","volume":"36","author":"Anwar","year":"2025","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.ipm.2026.104708_bib0005","series-title":"2022\u202fIEEE\/CVF Conference on computer vision and pattern recognition workshops","first-page":"4175","article-title":"Denoising pretraining for semantic segmentation","author":"Brempong","year":"2022"},{"key":"10.1016\/j.ipm.2026.104708_bib0006","series-title":"European conference on computer vision","first-page":"324","article-title":"Denoiseg: Joint denoising and segmentation","author":"Buchholz","year":"2020"},{"key":"10.1016\/j.ipm.2026.104708_bib0007","series-title":"European conference on computer vision","first-page":"3","article-title":"Deep boosting for image denoising","author":"Chen","year":"2018"},{"key":"10.1016\/j.ipm.2026.104708_bib0008","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TIP.2023.3289049","article-title":"Tsdn: Two-stage raw denoising in the dark","volume":"32","author":"Chen","year":"2023","journal-title":"IEEE Transactions on Image Processing"},{"issue":"8","key":"10.1016\/j.ipm.2026.104708_bib0009","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoising by sparse 3-d transform-domain collaborative filtering","volume":"16","author":"Dabov","year":"2007","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2026.104708_bib0010","series-title":"2017\u202fIEEE Conference on computer vision and pattern recognition workshops","first-page":"80","article-title":"Image denoising via CNNs: An adversarial approach","author":"Divakar","year":"2017"},{"key":"10.1016\/j.ipm.2026.104708_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110372","article-title":"Flexible image denoising model with multi-layer conditional feature modulation","volume":"152","author":"Du","year":"2024","journal-title":"Pattern Recognition"},{"issue":"12","key":"10.1016\/j.ipm.2026.104708_bib0012","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TIP.2006.881969","article-title":"Image denoising via sparse and redundant representations over learned dictionaries","volume":"15","author":"Elad","year":"2006","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2026.104708_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110563","article-title":"Bidirectional image denoising with blurred image feature","volume":"153","author":"Fan","year":"2024","journal-title":"Pattern Recognition"},{"issue":"9","key":"10.1016\/j.ipm.2026.104708_bib0014","doi-asserted-by":"crossref","first-page":"3956","DOI":"10.1109\/TNNLS.2020.3016321","article-title":"Multilevel edge features guided network for image denoising","volume":"32","author":"Fang","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.ipm.2026.104708_bib0015","unstructured":"Franzen, R. (1999). Kodak lossless true color image suite. 4, 9. http:\/\/r0k.us\/graphics\/kodak."},{"key":"10.1016\/j.ipm.2026.104708_bib0016","series-title":"Advances in neural information processing systems","article-title":"Multi-scale adaptive network for single image denoising","author":"Gou","year":"2022"},{"key":"10.1016\/j.ipm.2026.104708_bib0017","series-title":"2014\u202fIEEE Conference on computer vision and pattern recognition","first-page":"2862","article-title":"Weighted nuclear norm minimization with application to image denoising","author":"Gu","year":"2014"},{"key":"10.1016\/j.ipm.2026.104708_bib0018","series-title":"2019\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"1712","article-title":"Toward convolutional blind denoising of real photographs","author":"Guo","year":"2019"},{"key":"10.1016\/j.ipm.2026.104708_bib0019","series-title":"2024\u202fIEEE\/CVF Winter conference on applications of computer vision","first-page":"7523","article-title":"Robust object detection in challenging weather conditions","author":"Gupta","year":"2024"},{"issue":"3","key":"10.1016\/j.ipm.2026.104708_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103700","article-title":"Mlrn: A multi-view local reconstruction network for single image restoration","volume":"61","author":"Hao","year":"2024","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104708_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127799","article-title":"Efficient image denoising with heterogeneous kernel-based CNN","volume":"592","author":"Hu","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ipm.2026.104708_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114593","article-title":"Rust: Residual u-shaped transformer to approximate taylor expansion for image denoising","volume":"330","author":"Huang","year":"2025","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"10.1016\/j.ipm.2026.104708_bib0023","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1137\/120874989","article-title":"A nonlocal bayesian image denoising algorithm","volume":"6","author":"Lebrun","year":"2013","journal-title":"SIAM Journal on Imaging Sciences"},{"key":"10.1016\/j.ipm.2026.104708_bib0024","series-title":"2022\u202fIEEE\/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.ipm.2026.104708_bib0025","series-title":"2025\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"7491","article-title":"MaIR: A locality-and continuity-preserving mamba for image restoration","author":"Li","year":"2025"},{"key":"10.1016\/j.ipm.2026.104708_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113981","article-title":"Mdginet: Multi-frequency dynamic guidance and interaction network for image denoising","volume":"325","author":"Li","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104708_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106378","article-title":"Ewt: Efficient wavelet-transformer for single image denoising","volume":"177","author":"Li","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.ipm.2026.104708_bib0028","series-title":"2017\u202fIEEE Conference on computer vision and pattern recognition workshops","first-page":"136","article-title":"Enhanced deep residual networks for single image super-resolution","author":"Lim","year":"2017"},{"key":"10.1016\/j.ipm.2026.104708_bib0029","series-title":"2018\u202fIEEE\/CVF Conference on computer vision and pattern recognition workshops","first-page":"886","article-title":"Multi-level wavelet-CNN for image restoration","author":"Liu","year":"2018"},{"key":"10.1016\/j.ipm.2026.104708_bib0030","series-title":"2020\u202fIEEE\/CVF Conference on computer vision and pattern recognition workshops","first-page":"2140","article-title":"Gradnet image denoising","author":"Liu","year":"2020"},{"key":"10.1016\/j.ipm.2026.104708_bib0031","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/TIP.2022.3150294","article-title":"Meta PID attention network for flexible and efficient real-world noisy image denoising","volume":"31","author":"Ma","year":"2022","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2026.104708_bib0032","doi-asserted-by":"crossref","first-page":"3707","DOI":"10.1109\/TIP.2024.3404253","article-title":"Learning attention in the frequency domain for flexible real photograph denoising","volume":"33","author":"Ma","year":"2024","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2026.104708_bib0033","series-title":"Proceedings eighth IEEE international conference on computer vision","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"},{"issue":"4","key":"10.1016\/j.ipm.2026.104708_bib0034","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1134\/S1054661817040149","article-title":"Towards reliable object detection in noisy images","volume":"27","author":"Milyaev","year":"2017","journal-title":"Pattern Recognition and Image Analysis"},{"key":"10.1016\/j.ipm.2026.104708_bib0035","series-title":"2017\u202fIEEE Conference on computer vision and pattern recognition","first-page":"2750","article-title":"Benchmarking denoising algorithms with real photographs","author":"Ploetz","year":"2017"},{"key":"10.1016\/j.ipm.2026.104708_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109587","article-title":"Layered input gradinet for image denoising","volume":"254","author":"Qiao","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104708_bib0037","series-title":"2021\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"8596","article-title":"Adaptive consistency prior based deep network for image denoising","author":"Ren","year":"2021"},{"key":"10.1016\/j.ipm.2026.104708_bib0038","series-title":"Medical image computing and computer-assisted intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.ipm.2026.104708_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109603","article-title":"Multi scale pixel attention and feature extraction based neural network for image denoising","volume":"141","author":"Thakur","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2026.104708_bib0040","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","article-title":"Attention-guided CNN for image denoising","volume":"124","author":"Tian","year":"2020","journal-title":"Neural Networks"},{"key":"10.1016\/j.ipm.2026.104708_bib0041","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.neunet.2019.08.022","article-title":"Image denoising using deep CNN with batch renormalization","volume":"121","author":"Tian","year":"2020","journal-title":"Neural Networks"},{"key":"10.1016\/j.ipm.2026.104708_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106949","article-title":"Designing and training of a dual CNN for image denoising","volume":"226","author":"Tian","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.ipm.2026.104708_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109050","article-title":"Multi-stage image denoising with the wavelet transform","volume":"134","author":"Tian","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2026.104708_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102043","article-title":"A cross transformer for image denoising","volume":"102","author":"Tian","year":"2024","journal-title":"Information Fusion"},{"key":"10.1016\/j.ipm.2026.104708_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111116","article-title":"Restornet: An efficient network for multiple degradation image restoration","volume":"282","author":"Wang","year":"2023","journal-title":"Knowledge-Based Systems"},{"issue":"2, Part B","key":"10.1016\/j.ipm.2026.104708_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104380","article-title":"Dsrir: Dynamic spatial refinement learning for progressive all-in-one image restoration","volume":"63","author":"Wang","year":"2026","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2026.104708_bib0047","series-title":"2025\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"12679","article-title":"Complementary advantages: Exploiting cross-field frequency correlation for NIR-assisted image denoising","author":"Wang","year":"2025"},{"key":"10.1016\/j.ipm.2026.104708_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110291","article-title":"Dual residual attention network for image denoising","volume":"149","author":"Wu","year":"2024","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2026.104708_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110176","article-title":"Node-imgnet: A pde-informed effective and robust model for image denoising","volume":"148","author":"Xie","year":"2024","journal-title":"Pattern Recognition"},{"issue":"2","key":"10.1016\/j.ipm.2026.104708_bib0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3548459","article-title":"Synergy between semantic segmentation and image denoising via alternate boosting","volume":"19","author":"Xu","year":"2023","journal-title":"ACM Transactions on Multimedia Computing, Communications and Applications"},{"key":"10.1016\/j.ipm.2026.104708_bib0051","series-title":"European conference on computer vision","first-page":"41","article-title":"Dual adversarial network: Toward real-world noise removal and noise generation","author":"Yue","year":"2020"},{"key":"10.1016\/j.ipm.2026.104708_bib0052","series-title":"2020\u202fIEEE\/CVF Conference on computer vision and pattern recognition","first-page":"2693","article-title":"CycleISP: Real image restoration via improved data synthesis","author":"Zamir","year":"2020"},{"issue":"7","key":"10.1016\/j.ipm.2026.104708_bib0053","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Transactions on Image Processing"},{"issue":"9","key":"10.1016\/j.ipm.2026.104708_bib0054","doi-asserted-by":"crossref","first-page":"4608","DOI":"10.1109\/TIP.2018.2839891","article-title":"Ffdnet: Toward a fast and flexible solution for cnn-based image denoising","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2026.104708_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120628","article-title":"A parallel and serial denoising network","volume":"231","author":"Zhang","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104708_bib0056","article-title":"Learning multi-scale spatial-frequency features for image denoising","author":"Zhao","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2026.104708_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127924","article-title":"Gcstormer: Gated swin transformer with channel weights for image denoising","volume":"284","author":"Zhu","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.ipm.2026.104708_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111320","article-title":"Cfnet: Conditional filter learning with dynamic noise estimation for real image denoising","volume":"284","author":"Zuo","year":"2024","journal-title":"Knowledge-Based Systems"}],"container-title":["Information Processing & Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326000993?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457326000993?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T07:02:09Z","timestamp":1775545329000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457326000993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":58,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S0306457326000993"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104708","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Progressive feature refinement network for blind gaussian image denoising","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2026.104708","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":"104708"}}