Cover Image

DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Hengkang, Zhang, Xu, Li, Taihui, Wan, Yuxiang, Chen, Tiancong, Sun, Ju
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FMPlug: Plug-In Foundation Flow-Matching Priors for Inverse Problems
by: Wan, Yuxiang, et al.
Published: (2025)

Saving Foundation Flow-Matching Priors for Inverse Problems
by: Wan, Yuxiang, et al.
Published: (2025)

SAIP: A Plug-and-Play Scale-adaptive Module in Diffusion-based Inverse Problems
by: Wang, Lingyu, et al.
Published: (2025)

Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance
by: Mohseni-Sehdeh, Saeed, et al.
Published: (2025)

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models
by: Zheng, Yang, et al.
Published: (2025)

Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
by: Daras, Giannis, et al.
Published: (2024)

Solving Video Inverse Problems Using Image Diffusion Models
by: Kwon, Taesung, et al.
Published: (2024)

Align & Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment
by: Sfountouris, Loukas, et al.
Published: (2025)

Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing
by: Zhang, Bingliang, et al.
Published: (2024)

Rethinking Transfer Learning for Medical Image Classification
by: Peng, Le, et al.
Published: (2021)

Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
by: Aali, Asad, et al.
Published: (2024)

Repulsive Latent Score Distillation for Solving Inverse Problems
by: Zilberstein, Nicolas, et al.
Published: (2024)

Incorporating Pre-trained Diffusion Models in Solving the Schrödinger Bridge Problem
by: Tang, Zhicong, et al.
Published: (2025)

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models
by: Chung, Hyungjin, et al.
Published: (2022)

Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers
by: Xu, Tongda, et al.
Published: (2024)

Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
by: Peng, Xinyu, et al.
Published: (2024)

Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
by: Dontas, Michail, et al.
Published: (2024)

Inverse Problems with Diffusion Models: A MAP Estimation Perspective
by: Gutha, Sai Bharath Chandra, et al.
Published: (2024)

G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving
by: Murata, Naoki, et al.
Published: (2024)

SILO: Solving Inverse Problems with Latent Operators
by: Raphaeli, Ron, et al.
Published: (2025)

Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling
by: Liu, Gongye, et al.
Published: (2023)

Bayesian Conditioned Diffusion Models for Inverse Problems
by: Güngör, Alper, et al.
Published: (2024)

A Survey on Diffusion Models for Inverse Problems
by: Daras, Giannis, et al.
Published: (2024)

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
by: Chen, Eric, et al.
Published: (2025)

An Over Complete Deep Learning Method for Inverse Problems
by: Eliasof, Moshe, et al.
Published: (2024)

Contrastive Diffusion Guidance for Spatial Inverse Problems
by: Basu, Sattwik, et al.
Published: (2025)

Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
by: Wu, Zihui, et al.
Published: (2024)

SpeedUpNet: A Plug-and-Play Adapter Network for Accelerating Text-to-Image Diffusion Models
by: Chai, Weilong, et al.
Published: (2023)

PRISM: Probabilistic and Robust Inverse Solver with Measurement-Conditioned Diffusion Prior for Blind Inverse Problems
by: Hu, Yuanyun, et al.
Published: (2025)

On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods
by: Iagaru, David, et al.
Published: (2026)

Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements
by: Zhou, Shijie, et al.
Published: (2024)

Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models
by: Li, Dongzhuo
Published: (2021)

Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models
by: Kwon, Taesung, et al.
Published: (2026)

Improving Diffusion Models for Inverse Problems using Manifold Constraints
by: Chung, Hyungjin, et al.
Published: (2022)

Momentum Guidance: Plug-and-Play Guidance for Flow Models
by: Liao, Runlong, et al.
Published: (2026)

Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers
by: Hyoseok, Lee, et al.
Published: (2026)

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach
by: Chen, Haoxuan, et al.
Published: (2025)

Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
by: Jia, Jing, et al.
Published: (2026)

Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
by: Meng, Xiangming, et al.
Published: (2022)

Fast Samplers for Inverse Problems in Iterative Refinement Models
by: Pandey, Kushagra, et al.
Published: (2024)