Image Inverse Problems

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10 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

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Principled Design of Diffusion-based Optimizers for Inverse Problems
May 12, 2026Julio Oscanoa, Irmak Sivgin, Cagan Alkan +4Inference-Time Optimization Diffusion-Based Inverse Problems
Department of Bioengineering · Deparment of Electrical Engineering · Department of Radiology, Stanford University, USA.
A Stability Benchmark of Generative Regularizers for Inverse Problems
May 11, 2026Alexander Denker, Johannes Hertrich, Sebastian NeumayerGenerative Modeling Diffusion-Based Inverse Problems
Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, Germany · ENS Paris & Inria, France · Chemnitz University of Technology, Germany
Dual-Path Hyperprior Informed Deep Unfolding Network for Image Compressive Sensing
May 10, 2026Tianyi Lu, Wenxue Cui, Shaohui LiuImage Reconstruction Image Inverse Problems
Harbin Institute of Technology
Outlier-Robust Diffusion Solvers for Inverse Problems
May 10, 2026Yang Zheng, Jiahua Liu, Tongyao Pang +2Diffusion Models for Image Restoration Robust Optimization
School of Computer Science and Engineering, University of Electronic Science and Technology of China · Yau Mathematical Sciences Center, Tsinghua University
P-Flow: Proxy-gradient Flows for Linear Inverse Problems
May 8, 2026Zehua Jiang, Fenghao Zhu, Xinquan Wang +2Flow Matching Inverse Problems
University of Notre Dame Indiana, USA
Consistency Regularised Gradient Flows for Inverse Problems
May 8, 2026Alessio Spagnoletti, Tim Y. J. Wang, Marcelo Pereyra +1Latent Diffusion Models Wasserstein Gradient Flows
Laboratoire MAP5, UMR 8145, Université Paris Cité, CNRS · Department of Mathematics, Imperial College London · Heriot-Watt University, MACS & Maxwell Institute
Conditions for well-posed color recovery in scattering media
May 5, 2026Grigory Solomatov, Derya AkkaynakImage Restoration Inverse Problems
Hatter Department of Marine Technologies, University of Haifa, Haifa, Israel · Interuniversity Institute for Marine Sciences, Eilat, Israel
Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction
Apr 29, 2026Carson Yu Liu, Jun Cheng, Chien-Chun Chen +1Image Reconstruction Image Inverse Problems
School of Electrical and Computer Engineering, University of Sydney, Camperdown, NSW 2006, Australia · Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632 · Department of Engineering and System Science, National Tsing Hua University, Hsinchu 300044, Taiwan
GS-DOT: Gaussian splatting-based image reconstruction for diffuse optical tomography
Apr 26, 2026Jingjing JiangMedical Imaging Image Reconstruction
Computational Optical Imaging Group, Biomedical Optics Research Laboratory, Dept. of Neonatology, University Hospital Zurich and University of Zurich, Switzerland
Multiscale Super Resolution without Image Priors
Apr 23, 2026Daniel Fu, Gabby Litterio, Pedro Felzenszwalb +1Image Super-Resolution Image Inverse Problems
Division of Applied Mathematics (Fu) and School of Engineering (Litterio, Felzenszwalb, Zia), Brown University, Providence, RI, 02906.
Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts
Apr 21, 2026Hanna Pulkkinen, Jenni Poimala, Leonid Kunyansky +2Medical Imaging Image Reconstruction
1Research Unit of Mathematical Sciences, University of Oulu, Finland · Department of Technical Physics, University of Eastern Finland, Finland · Department of Mathematics, University of Arizona, USA +2
Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning
Apr 18, 2026Yingzhi Xia, Setthakorn Tanomkiattikun, Liangli Zhen +1Markov Chain Monte Carlo Bayesian Inverse Problems
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Agency for Science, Technology and Research, Singapore · Johns Hopkins University +1
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
Apr 17, 2026Markus Haltmeier, Lukas Neumann, Nadja Gruber +1Self-Supervised Learning Sparse-View CT Reconstruction
3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems
Mar 2, 2026Narges Moeini, Namhoon Kim, Justin Romberg +1Image Inverse Problems Image Denoising
Georgia Institute of Technology
Allure of Craquelure: A Variational-Generative Approach to Crack Detection in Paintings
Feb 10, 2026Laura Paul, Holger Rauhut, Martin Burger +2Crack Segmentation Image Inverse Problems
Dept. of Mathematics, LMU Munich, Germany · Munich Center for Machine Learning, Germany · Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, Germany +2
Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
Feb 9, 2026Alexander Denker, Zeljko Kereta, Carola-Bibiane Schönlieb +1Generative Flow Networks Flow-Based Generative Modeling
Department of Computer Science, University College London · Department of Applied Mathematics and Theoretical Physics, University of Cambridge.
Inverse Problems Conditioned on Observation Ensembles: Applications and Methods
Jan 29, 2026Zhengyan Huan, Camila Pazos, Martin Klassen +3Posterior Sampling Inverse Problems
Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts · The NSF AI Institute for Artificial Intelligence and Fundamental Interactions · Department of Physics and Astronomy, Tufts University, Medford, Massachusetts +1
Learned iterative networks: An operator learning perspective
Dec 9, 2025Andreas Hauptmann, Ozan ÖktemImage Reconstruction Computational Imaging
Research Unit of Mathematical Sciences, University of Oulu, Finland · Department of Computer Science, University College London, United Kingdom · Department of Mathematics, KTH - Royal Institute of Technology, Sweden
Jacobian-Aware Posterior Sampling for Inverse Problems
Nov 23, 2025Liav Hen, Tom Tirer, Raja Giryes +1Bayesian Inverse Problems Diffusion Guidance
Tel Aviv University, Israel · Bar-Ilan University, Israel · University of Cambridge, UK
DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
Nov 21, 2025Hao Chen, Renzheng Zhang, Scott S. HowardDiffusion Models for Image Restoration Bayesian Inverse Problems
University of Notre Dame, Notre Dame, IN 46556, USA
MACS: Measurement-Aware Consistency Sampling for Inverse Problems
Oct 2, 2025Amirreza Tanevardi, Pooria Abbas Rad Moghadam, Seyed Mohammad Eshtehardian +2Diffusion Model Sampling Inverse Problems
Department of Electrical Engineering Sharif University of Technology Tehran, Iran · Electronics Research Institute Sharif University of Technology Tehran, Iran
Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers
Sep 18, 2025Stanislas Ducotterd, Zhiyuan Hu, Michael Unser +1Image Inverse Problems
Biomedical Imaging Group, ´Ecole Polytechnique F´ed´erale de Lausanne
A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP
Aug 4, 2025Minh-Hai Nguyen, Edouard Pauwels, Pierre WeissImage Deblurring Bayesian Inference
IRIT, Toulouse University, Toulouse, France · Toulouse School of Economics, Universit´e Toulouse Capitole et IUF, Toulouse, France
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
May 29, 2025Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi +4Diffusion Models for Image Restoration Diffusion-Based Inverse Problems
University of Alberta · Alberta Machine, Intelligence Institute (Amii) · Neuroscience and Mental Health Institute, University of Alberta +3
Filterless Snapshot Hyperspectral Imaging using Guided Patch Diffusion
Dec 3, 2024Dean Hazineh, Luca Sacchi, Davide Cassara +2Diffusion Model Guidance Physics-Guided Image Restoration
Harvard University

May 12, 2026, cs.CVJ/K move · Enter open · S save

Principled Design of Diffusion-based Optimizers for Inverse Problems

Julio Oscanoa, Irmak Sivgin, Cagan Alkan, Daniel Ennis+3

Department of Bioengineering · Deparment of Electrical Engineering · Department of Radiology, Stanford University, USA.

Inference-Time Optimization Diffusion-Based Inverse Problems Diffusion Posterior Sampling Diffusion Model Inference Acceleration Inverse Problems Image Inverse Problems

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Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference times and cumbersome hyperparameter tuning. While pretrained diffusion models can be reused across tasks without retraining, inference-time hyperparameters such as the noise schedule and posterior sampling weights typically require ad-hoc adjustment for each problem setup. We propose principled reparameterizations that induce invariances, allowing the same hyperparameters to be reused across multiple problems without re-tuning. In addition, building on the RED-diff framework, which reformulates posterior sampling as an optimization problem, we further develop the OptDiff pipeline. OptDiff provides a simplified tuning framework that facilitates the integration of convex optimization tools to accelerate inference. Experiments on image reconstruction, deblurring, and super-resolution show substantial speedups and improved image quality.

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