{"total":61,"rows":[{"id":"2407.01521","title":"Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing","created":"2024-07-01","cited_by_count":163,"area":"diffusion"},{"id":"2405.18782","title":"Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors","created":"2024-05-29","cited_by_count":97,"area":"diffusion"},{"id":"2410.02423","title":"PnP-Flow: Plug-and-Play Image Restoration with Flow Matching","created":"2024-10-03","cited_by_count":97,"area":"diffusion"},{"id":"2503.11043","title":"InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences","created":"2025-03-14","cited_by_count":68,"area":"diffusion"},{"id":"2408.02320","title":"A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models","created":"2024-08-05","cited_by_count":65,"area":"diffusion"},{"id":"2405.16749","title":"DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models","created":"2024-05-27","cited_by_count":61,"area":"diffusion"},{"id":"2405.15986","title":"Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity","created":"2024-05-24","cited_by_count":42,"area":"diffusion"},{"id":"2503.01161","title":"Split Gibbs Discrete Diffusion Posterior Sampling","created":"2025-03-03","cited_by_count":18,"area":"diffusion"},{"id":"2506.03979","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","created":"2025-06-04","cited_by_count":18,"area":"diffusion"},{"id":"2510.27562","title":"Optimal Convergence Analysis of DDPM for General Distributions","created":"2025-10-31","cited_by_count":16,"area":"diffusion"},{"id":"2508.14807","title":"Source-Guided Flow Matching","created":"2025-08-20","cited_by_count":13,"area":"diffusion"},{"id":"2601.23280","title":"Decoupled Diffusion Sampling for Inverse Problems on Function Spaces","created":"2026-01-30","cited_by_count":10,"area":"diffusion"},{"id":"2508.16306","title":"A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions","created":"2025-08-22","cited_by_count":8,"area":"diffusion"},{"id":"2405.15676","title":"Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems","created":"2024-05-24","cited_by_count":7,"area":"diffusion"},{"id":"2410.13746","title":"Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers","created":"2024-10-17","cited_by_count":7,"area":"diffusion"},{"id":"2503.20297","title":"Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model","created":"2025-03-26","cited_by_count":5,"area":"diffusion"},{"id":"2505.18276","title":"Preconditioned Langevin Dynamics with Score-Based Generative Models for Infinite-Dimensional Linear Bayesian Inverse Problems","created":"2025-05-23","cited_by_count":5,"area":"diffusion"},{"id":"2506.10955","title":"ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems","created":"2025-06-12","cited_by_count":5,"area":"diffusion"},{"id":"2509.22736","title":"PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems","created":"2025-09-25","cited_by_count":5,"area":"diffusion"},{"id":"2510.10968","title":"Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors","created":"2025-10-13","cited_by_count":5,"area":"diffusion"},{"id":"2412.05488","title":"Enhancing Sample Generation of Diffusion Models using Noise Level Correction","created":"2024-12-07","cited_by_count":4,"area":"diffusion"},{"id":"2607.19333","title":"Provable diffusion-based posterior sampling for linear inverse problems via DDIM","created":"2026-07-21","cited_by_count":4,"area":"diffusion"},{"id":"2410.05143","title":"Leveraging Multimodal Diffusion Models to Accelerate Imaging with Side Information","created":"2024-10-07","cited_by_count":3,"area":"diffusion"},{"id":"2504.07549","title":"STeP: A Framework for Solving Scientific Video Inverse Problems with Spatiotemporal Diffusion Priors","created":"2025-04-10","cited_by_count":3,"area":"diffusion"},{"id":"2508.07631","title":"Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo","created":"2025-08-11","cited_by_count":3,"area":"diffusion"},{"id":"2509.03898","title":"Diffusion Generative Models Meet Compressed Sensing, with Applications to Imaging and Finance","created":"2025-09-04","cited_by_count":3,"area":"diffusion"},{"id":"2509.12857","title":"Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling","created":"2025-09-16","cited_by_count":3,"area":"diffusion"},{"id":"2509.20511","title":"A Recovery Theory for Diffusion Priors: Deterministic Analysis of the Implicit Prior Algorithm","created":"2025-09-24","cited_by_count":3,"area":"diffusion"},{"id":"2512.04283","title":"Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint","created":"2025-12-03","cited_by_count":3,"area":"diffusion"},{"id":"2512.18367","title":"PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Prior","created":"2025-12-20","cited_by_count":3,"area":"diffusion"},{"id":"2604.03603","title":"Stochastic Generative Plug-and-Play Priors","created":"2026-04-04","cited_by_count":3,"area":"diffusion"},{"id":"2605.18745","title":"SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate","created":"2026-05-18","cited_by_count":3,"area":"diffusion"},{"id":"2505.01049","title":"Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees","created":"2025-05-02","cited_by_count":2,"area":"diffusion"},{"id":"2505.22923","title":"Plug-and-Play Posterior Sampling for Blind Inverse Problems","created":"2025-05-28","cited_by_count":2,"area":"diffusion"},{"id":"2506.13614","title":"Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models","created":"2025-06-16","cited_by_count":2,"area":"diffusion"},{"id":"2509.04860","title":"Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging","created":"2025-09-05","cited_by_count":2,"area":"diffusion"},{"id":"2601.19368","title":"Divergence-Free Diffusion Models for Incompressible Fluid Flows","created":"2026-01-27","cited_by_count":2,"area":"diffusion"},{"id":"2603.01253","title":"Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography","created":"2026-03-01","cited_by_count":2,"area":"diffusion"},{"id":"2605.06538","title":"Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability","created":"2026-05-07","cited_by_count":2,"area":"diffusion"},{"id":"2501.17468","title":"Solving Inverse Problems using Diffusion with Iterative Colored Renoising","created":"2025-01-29","cited_by_count":1,"area":"diffusion"},{"id":"2506.09416","title":"Noise Conditional Variational Score Distillation","created":"2025-06-11","cited_by_count":1,"area":"diffusion"},{"id":"2507.21260","title":"Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors","created":"2025-07-28","cited_by_count":1,"area":"diffusion"},{"id":"2512.02768","title":"Diffusion-Prior Split Gibbs Sampling for Synthetic Aperture Radar Imaging under Incomplete Measurements","created":"2025-12-02","cited_by_count":1,"area":"diffusion"},{"id":"2601.21200","title":"Provably Reliable Classifier Guidance via Cross-Entropy Control","created":"2026-01-29","cited_by_count":1,"area":"diffusion"},{"id":"2602.07715","title":"Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models","created":"2026-02-07","cited_by_count":1,"area":"diffusion"},{"id":"2602.10792","title":"Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling","created":"2026-02-11","cited_by_count":1,"area":"diffusion"},{"id":"2603.01591","title":"FAST-DIPS: Adjoint-Free Analytic Steps and Hard-Constrained Likelihood Correction for Diffusion-Prior Inverse Problems","created":"2026-03-02","cited_by_count":1,"area":"diffusion"},{"id":"2606.24516","title":"What Do Flow-Based Inverse Solvers Approximate? A Posterior-Transport View","created":"2026-06-23","cited_by_count":1,"area":"diffusion"},{"id":"2503.22563","title":"RELD: Regularization by Latent Diffusion Models for Image Restoration","created":"2025-03-28","cited_by_count":0,"area":"diffusion"},{"id":"2510.14114","title":"Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey","created":"2025-10-15","cited_by_count":0,"area":"diffusion"},{"id":"2511.18473","title":"Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements","created":"2025-11-23","cited_by_count":0,"area":"diffusion"},{"id":"2512.23232","title":"SURE Guided Posterior Sampling: Trajectory Correction for Diffusion-Based Inverse Problems","created":"2025-12-29","cited_by_count":0,"area":"diffusion"},{"id":"2602.00176","title":"Posterior Continuation with Noise-Conditioned Frequency Exposure for Diffusion Inverse Problems","created":"2026-01-30","cited_by_count":0,"area":"diffusion"},{"id":"2602.23214","title":"Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction","created":"2026-02-26","cited_by_count":0,"area":"diffusion"},{"id":"2604.22212","title":"Multimodal Diffusion to Mutually Enhance Polarized Light and Low Resolution EBSD Data","created":"2026-04-24","cited_by_count":0,"area":"diffusion"},{"id":"2605.28711","title":"Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Models","created":"2026-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2605.31596","title":"KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems","created":"2026-05-29","cited_by_count":0,"area":"diffusion"},{"id":"2606.03212","title":"Bayesian Tensor Decomposition with Diffusion Model Prior","created":"2026-06-02","cited_by_count":0,"area":"diffusion"},{"id":"2609.20769","title":"FlowSGS: Improving Flow Matching Priors for Inverse Imaging with Stochastic Interpolants","created":"2026-09-17","cited_by_count":0,"area":"diffusion"},{"id":"2609.21251","title":"Geometry-Aware Diffusion Guidance via Curvature-Adaptive Tubular Correction","created":"2026-09-18","cited_by_count":0,"area":"diffusion"},{"id":"2609.30539","title":"Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding","created":"2026-09-24","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}