{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T13:49:44Z","timestamp":1776174584458,"version":"3.50.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3804500"],"award-info":[{"award-number":["2023YFC3804500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11901220"],"award-info":[{"award-number":["11901220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Signal Processing: Image Communication"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.image.2026.117532","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T20:30:19Z","timestamp":1772483419000},"page":"117532","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MAP-based problem-agnostic diffusion model for inverse problems"],"prefix":"10.1016","volume":"144","author":[{"given":"Pingping","family":"Tao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0827-9803","authenticated-orcid":false,"given":"Haixia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.image.2026.117532_b1","doi-asserted-by":"crossref","unstructured":"J. Choi, S. Kim, Y. Jeong, Y. Gwon, S. Yoon, ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models, in: International Conference on Computer Vision, 2021, pp. 14347\u201314356.","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"10.1016\/j.image.2026.117532_b2","series-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020"},{"key":"10.1016\/j.image.2026.117532_b3","series-title":"Solving inverse problems in medical imaging with score-based generative models","author":"Song","year":"2021"},{"key":"10.1016\/j.image.2026.117532_b4","doi-asserted-by":"crossref","first-page":"25683","DOI":"10.52202\/068431-1862","article-title":"Improving diffusion models for inverse problems using manifold constraints","volume":"35","author":"Chung","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.image.2026.117532_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3626235","article-title":"Diffusion models: A comprehensive survey of methods and applications","volume":"56","author":"Yang","year":"2023","journal-title":"ACM Comput. Surv."},{"issue":"9","key":"10.1016\/j.image.2026.117532_b6","doi-asserted-by":"crossref","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: A survey","volume":"45","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117532_b7","doi-asserted-by":"crossref","unstructured":"R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-resolution image synthesis with latent diffusion models, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.image.2026.117532_b8","series-title":"Understanding diffusion models: A unified perspective","author":"Luo","year":"2022"},{"key":"10.1016\/j.image.2026.117532_b9","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2025.3569700","article-title":"Efficient diffusion models: A comprehensive survey from principles to practices","author":"Ma","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117532_b10","series-title":"Diffusion models and representation learning: A survey","author":"Fuest","year":"2024"},{"key":"10.1016\/j.image.2026.117532_b11","series-title":"An overview of diffusion models: Applications, guided generation, statistical rates and optimization","author":"Chen","year":"2024"},{"key":"10.1016\/j.image.2026.117532_b12","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2024.3361474","article-title":"A survey on generative diffusion models","author":"Cao","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.image.2026.117532_b13","series-title":"Diffusion models for medical image analysis: A comprehensive survey","author":"Kazerouni","year":"2022"},{"issue":"4","key":"10.1016\/j.image.2026.117532_b14","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10462-025-11110-3","article-title":"Comprehensive exploration of diffusion models in image generation: a survey","volume":"58","author":"Chen","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.image.2026.117532_b15","series-title":"Diffusion models for image restoration and enhancement\u2013a comprehensive survey","author":"Li","year":"2023"},{"key":"10.1016\/j.image.2026.117532_b16","article-title":"Diffusion models, image super-resolution, and everything: A survey","author":"Moser","year":"2024","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.image.2026.117532_b17","series-title":"Efficient diffusion models: A survey","author":"Shen","year":"2025"},{"issue":"4","key":"10.1016\/j.image.2026.117532_b18","first-page":"4713","article-title":"Image super-resolution via iterative refinement","volume":"45","author":"Saharia","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.image.2026.117532_b19","doi-asserted-by":"crossref","unstructured":"C. Saharia, W. Chan, H. Chang, C. Lee, J. Ho, T. Salimans, D. Fleet, M. Norouzi, Palette: Image-to-image diffusion models, in: ACM SIGGRAPH 2022 Conference Proceedings, 2022, pp. 1\u201310.","DOI":"10.1145\/3528233.3530757"},{"key":"10.1016\/j.image.2026.117532_b20","doi-asserted-by":"crossref","unstructured":"J. Whang, M. Delbracio, H. Talebi, C. Saharia, A.G. Dimakis, P. Milanfar, Deblurring via stochastic refinement, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16293\u201316303.","DOI":"10.1109\/CVPR52688.2022.01581"},{"key":"10.1016\/j.image.2026.117532_b21","first-page":"23593","article-title":"Denoising diffusion restoration models","volume":"35","author":"Kawar","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117532_b22","series-title":"Diffusion posterior sampling for general noisy inverse problems","author":"Chung","year":"2022"},{"key":"10.1016\/j.image.2026.117532_b23","unstructured":"J. Song, A. Vahdat, M. Mardani, J. Kautz, Pseudoinverse-guided diffusion models for inverse problems, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.image.2026.117532_b24","unstructured":"X. Meng, Y. Kabashima, Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems, in: Asian Conference on Machine Learning, 2025, pp. 623\u2013638."},{"key":"10.1016\/j.image.2026.117532_b25","first-page":"13242","article-title":"Stochastic solutions for linear inverse problems using the prior implicit in a denoiser","volume":"34","author":"Kadkhodaie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117532_b26","doi-asserted-by":"crossref","unstructured":"H. Chung, J. Kim, S. Kim, J.C. Ye, Parallel diffusion models of operator and image for blind inverse problems, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6059\u20136069.","DOI":"10.1109\/CVPR52729.2023.00587"},{"key":"10.1016\/j.image.2026.117532_b27","first-page":"14938","article-title":"Robust compressed sensing mri with deep generative priors","volume":"34","author":"Jalal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117532_b28","unstructured":"A. Bora, A. Jalal, E. Price, A.G. Dimakis, Compressed sensing using generative models, in: International Conference on Machine Learning, 2017, pp. 537\u2013546."},{"key":"10.1016\/j.image.2026.117532_b29","doi-asserted-by":"crossref","first-page":"14715","DOI":"10.52202\/068431-1070","article-title":"Diffusion models as plug-and-play priors","volume":"35","author":"Graikos","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117532_b30","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117532_b31","unstructured":"Z. Dou, Y. Song, Diffusion posterior sampling for linear inverse problem solving: A filtering perspective, in: The Twelfth International Conference on Learning Representations, 2024."},{"key":"10.1016\/j.image.2026.117532_b32","unstructured":"D. Thaker, A. Goyal, R. Vidal, Frequency-guided posterior sampling for diffusion-based image restoration, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 12873\u201312882."},{"key":"10.1016\/j.image.2026.117532_b33","series-title":"Denoising diffusion implicit models","author":"Song","year":"2020"},{"key":"10.1016\/j.image.2026.117532_b34","series-title":"Manifold preserving guided diffusion","author":"He","year":"2023"},{"key":"10.1016\/j.image.2026.117532_b35","first-page":"87647","article-title":"Learning diffusion priors from observations by expectation maximization","volume":"37","author":"Rozet","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.image.2026.117532_b36","series-title":"Guidance with spherical gaussian constraint for conditional diffusion","author":"Yang","year":"2024"},{"key":"10.1016\/j.image.2026.117532_b37","series-title":"Improving diffusion models for inverse problems using optimal posterior covariance","author":"Peng","year":"2024"},{"key":"10.1016\/j.image.2026.117532_b38","series-title":"Flowdps: Flow-driven posterior sampling for inverse problems","author":"Kim","year":"2025"},{"issue":"3","key":"10.1016\/j.image.2026.117532_b39","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/0304-4149(82)90051-5","article-title":"Reverse-time diffusion equation models","volume":"12","author":"Anderson","year":"1982","journal-title":"Stochastic Process. Appl."},{"key":"10.1016\/j.image.2026.117532_b40","series-title":"Quasi-taylor samplers for diffusion generative models based on ideal derivatives","author":"Tachibana","year":"2021"},{"issue":"7","key":"10.1016\/j.image.2026.117532_b41","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1162\/NECO_a_00142","article-title":"A connection between score matching and denoising autoencoders","volume":"23","author":"Vincent","year":"2011","journal-title":"Neural Comput."},{"key":"10.1016\/j.image.2026.117532_b42","unstructured":"S. Arjomand Bigdeli, M. Zwicker, P. Favaro, M. Jin, Deep mean-shift priors for image restoration, in: Advances in Neural Information Processing Systems, 2017, pp. 763\u2013772."}],"container-title":["Signal Processing: Image Communication"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092359652600055X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S092359652600055X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T13:00:16Z","timestamp":1776171616000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S092359652600055X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":42,"alternative-id":["S092359652600055X"],"URL":"https:\/\/doi.org\/10.1016\/j.image.2026.117532","relation":{},"ISSN":["0923-5965"],"issn-type":[{"value":"0923-5965","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MAP-based problem-agnostic diffusion model for inverse problems","name":"articletitle","label":"Article Title"},{"value":"Signal Processing: Image Communication","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.image.2026.117532","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":"117532"}}