{"paper":{"title":"Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"LAMP improves diffusion posterior sampling by adding a lagged temporal correction from second-order discretization while preserving the posterior structure.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Davide Evangelista, Elena Morotti, Francesco Pivi, Maurizio Gabbrielli","submitted_at":"2026-05-12T11:38:36Z","abstract_excerpt":"Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Yet, its standard formulations rely on instantaneous data-consistent estimates, which induce temporal variability in the reverse dynamics. We reinterpret PS from a dynamical perspective, showing that the standard PS update corresponds to a first-order discretization of the diffusion dynamics plus a residual correction capturing the mismatch between the denoised prediction and the data-consistent estimate. A second-order discretization, however, nat"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"LAMP preserves the structure of a posterior sampler and improves the reverse transition via a bias-variance trade-off, as shown by one-step risk analysis and consistent gains over DiffPIR and DDRM without increasing denoising evaluations.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the second-order discretization term remains stable and beneficial across the full reverse trajectory when combined with the residual correction, rather than only in the one-step analysis.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"LAMP improves diffusion posterior sampling by adding a lagged temporal correction from second-order discretization while preserving the posterior structure.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6b7c48f310e5d1038cfe08d862bebf127cd78b2253dfc8c58c65148a37fcf0dd"},"source":{"id":"2605.12573","kind":"arxiv","version":1},"verdict":{"id":"58d4ff02-a5b9-42e7-90f1-1d34546636dc","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T20:43:19.910008Z","strongest_claim":"LAMP preserves the structure of a posterior sampler and improves the reverse transition via a bias-variance trade-off, as shown by one-step risk analysis and consistent gains over DiffPIR and DDRM without increasing denoising evaluations.","one_line_summary":"LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the second-order discretization term remains stable and beneficial across the full reverse trajectory when combined with the residual correction, rather than only in the one-step analysis.","pith_extraction_headline":"LAMP improves diffusion posterior sampling by adding a lagged temporal correction from second-order discretization while preserving the posterior structure."},"references":{"count":17,"sample":[{"doi":"","year":2023,"title":"Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky, and Jong Chul Ye. Diffusion posterior sampling for general noisy inverse problems. InInternational Conference on Learning Representatio","work_id":"4e07eff4-123a-4a0d-83bf-2b0bc67ae774","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Improving diffusion models for inverse problems using manifold constraints.Advances in Neural Information Processing Systems, 35:25683–25696, 2022","work_id":"f867adf9-9c8b-4e3e-af5e-9ea543846030","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Diffusion models beat gans on image synthesis","work_id":"11a4fa70-50e4-4103-b58a-48259ce280d9","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Denoising diffusion probabilistic models","work_id":"007f689e-17d2-4add-8618-5321c85e056b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Denoising diffusion restoration models","work_id":"30098793-59a0-459f-8bf7-e8529608aca4","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":17,"snapshot_sha256":"999bd6045af544ab4b449bd0738a175538d0bb72a21df240f823301effc5e9a7","internal_anchors":0},"formal_canon":{"evidence_count":3,"snapshot_sha256":"bba6777d8612f7f1826c2113d97da25304779aa2bff6fdfc22ffade269a76bec"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}