{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-powerful-prior-knowledge-of-diffusion","title":"Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing","arxiv_id":"2503.08429","date":"2025-03-11","proceeding":"CVPR 2025 1","authors":["Chen Liao","Yan Shen","Dan Li","Zhongli Wang"],"abstract":"Recently, Deep Unfolding Networks (DUNs) have achieved impressive reconstruction quality in the field of image Compressive Sensing (CS) by unfolding iterative optimization algorithms into neural networks. The reconstruction quality of DUNs depends on the learned prior knowledge, so introducing stronger prior knowledge can further improve reconstruction quality. On the other hand, pre-trained diffusion models contain powerful prior knowledge and have a solid theoretical foundation and strong scalability, but it requires a large number of iterative steps to achieve reconstruction. In this paper, we propose to use the powerful prior knowledge of pre-trained diffusion model in DUNs to achieve high-quality reconstruction with less steps for image CS. Specifically, we first design an iterative optimization algorithm named Diffusion Message Passing (DMP), which embeds a pre-trained diffusion model into each iteration process of DMP. Then, we deeply unfold the DMP algorithm into a neural network named DMP-DUN. The proposed DMP-DUN can use lightweight neural networks to achieve mapping from measurement data to the intermediate steps of the reverse diffusion process and directly approximate the divergence of the diffusion model, thereby further improving reconstruction efficiency. Extensive experiments show that our proposed DMP-DUN achieves state-of-the-art performance and requires at least only 2 steps to reconstruct the image. Codes are available at https://github.com/FengodChen/DMP-DUN-CVPR2025.","url_abs":"https://arxiv.org/abs/2503.08429v1","url_pdf":"https://arxiv.org/pdf/2503.08429v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"using-powerful-prior-knowledge-of-diffusion","repo_url":"https://github.com/fengodchen/dmp-dun-cvpr2025","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"},{"task_slug":"image-compressed-sensing","task_name":"Image Compressed Sensing"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/compressive-sensing-on-set11-cs-50","task":"Compressive Sensing","dataset":"Set11 cs=50%","model":"DMP-DUN-Plus (4-step)","rank_in_archive_order":1,"of":2,"metrics":{"Average PSNR":"42.82","PSNR":"42.82"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2503.08429","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.08429"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/FengodChen/DMP-DUN-CVPR2025","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":1,"unverified":3},"by_repo_kind":{"official":{"samples":4,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"129b804760b3115f","entry":"zero_module","repo":"FengodChen/DMP-DUN-CVPR2025","repo_kind":"official","path":"models/Guided_Diffusion.py","file_url":"https://github.com/FengodChen/DMP-DUN-CVPR2025/blob/HEAD/models/Guided_Diffusion.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"129b804760b3115f"}},{"code_sha256_prefix":"51950b12318e7d94","entry":"avg_pool_nd","repo":"FengodChen/DMP-DUN-CVPR2025","repo_kind":"official","path":"models/Guided_Diffusion.py","file_url":"https://github.com/FengodChen/DMP-DUN-CVPR2025/blob/HEAD/models/Guided_Diffusion.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"51950b12318e7d94"}},{"code_sha256_prefix":"5c4a80c1dfd5bff3","entry":"cfg_gen","repo":"FengodChen/DMP-DUN-CVPR2025","repo_kind":"official","path":"configs/DMP_DUN_plus_2step.py","file_url":"https://github.com/FengodChen/DMP-DUN-CVPR2025/blob/HEAD/configs/DMP_DUN_plus_2step.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5c4a80c1dfd5bff3"}},{"code_sha256_prefix":"49ff3519834529f3","entry":"conv_nd","repo":"FengodChen/DMP-DUN-CVPR2025","repo_kind":"official","path":"models/Guided_Diffusion.py","file_url":"https://github.com/FengodChen/DMP-DUN-CVPR2025/blob/HEAD/models/Guided_Diffusion.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"49ff3519834529f3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}