Learning Normalized Energy Models for Linear Inverse Problems
Fuente:
arXiv
Saved in:
| Main Authors: | Zilberstein, Nicolas, Segarra, Santiago, Simoncelli, Eero, Guth, Florentin |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
by: Kadkhodaie, Zahra, et al.
Published: (2020)
by: Kadkhodaie, Zahra, et al.
Published: (2020)
Learning a distance measure from the information-estimation geometry of data
by: Ohayon, Guy, et al.
Published: (2025)
by: Ohayon, Guy, et al.
Published: (2025)
A polar prediction model for learning to represent visual transformations
by: Fiquet, Pierre-Étienne H., et al.
Published: (2023)
by: Fiquet, Pierre-Étienne H., et al.
Published: (2023)
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
by: Zhang, Ling-Qi, et al.
Published: (2024)
by: Zhang, Ling-Qi, et al.
Published: (2024)
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
by: Chen, Eric, et al.
Published: (2025)
by: Chen, Eric, et al.
Published: (2025)
Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling
by: Liu, Gongye, et al.
Published: (2023)
by: Liu, Gongye, et al.
Published: (2023)
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
by: Mohan, Sreyas, et al.
Published: (2020)
by: Mohan, Sreyas, et al.
Published: (2020)
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
by: Milanfar, Peyman, et al.
Published: (2024)
by: Milanfar, Peyman, et al.
Published: (2024)
Application-driven Validation of Posteriors in Inverse Problems
by: Adler, Tim J., et al.
Published: (2023)
by: Adler, Tim J., 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)
by: Hu, Yuanyun, et al.
Published: (2025)
MACS: Measurement-Aware Consistency Sampling for Inverse Problems
by: Tanevardi, Amirreza, et al.
Published: (2025)
by: Tanevardi, Amirreza, et al.
Published: (2025)
Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
by: Meanti, Giacomo, et al.
Published: (2025)
by: Meanti, Giacomo, et al.
Published: (2025)
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
by: Achituve, Idan, et al.
Published: (2025)
by: Achituve, Idan, et al.
Published: (2025)
SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problems
by: Alkhouri, Ismail, et al.
Published: (2024)
by: Alkhouri, Ismail, et al.
Published: (2024)
Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models
by: Su, Xun, et al.
Published: (2025)
by: Su, Xun, et al.
Published: (2025)
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images
by: Roy, Saikat, et al.
Published: (2025)
by: Roy, Saikat, et al.
Published: (2025)
Saving Foundation Flow-Matching Priors for Inverse Problems
by: Wan, Yuxiang, et al.
Published: (2025)
by: Wan, Yuxiang, et al.
Published: (2025)
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images
by: Saad, Muhammad Muneeb, et al.
Published: (2023)
by: Saad, Muhammad Muneeb, et al.
Published: (2023)
FMPlug: Plug-In Foundation Flow-Matching Priors for Inverse Problems
by: Wan, Yuxiang, et al.
Published: (2025)
by: Wan, Yuxiang, et al.
Published: (2025)
Repulsive Latent Score Distillation for Solving Inverse Problems
by: Zilberstein, Nicolas, et al.
Published: (2024)
by: Zilberstein, Nicolas, et al.
Published: (2024)
Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models
by: Taha, Ahmed M., et al.
Published: (2025)
by: Taha, Ahmed M., et al.
Published: (2025)
Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview
by: Auras, Alexander, et al.
Published: (2024)
by: Auras, Alexander, et al.
Published: (2024)
Dual Ascent Diffusion for Inverse Problems
by: Kim, Minseo, et al.
Published: (2025)
by: Kim, Minseo, et al.
Published: (2025)
Inverse Synthetic Aperture Fourier Ptychography
by: Chan, Matthew A., et al.
Published: (2025)
by: Chan, Matthew A., et al.
Published: (2025)
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems
by: Alçalar, Yaşar Utku, et al.
Published: (2024)
by: Alçalar, Yaşar Utku, et al.
Published: (2024)
Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation
by: Savant, Shamit
Published: (2026)
by: Savant, Shamit
Published: (2026)
Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior
by: González, Mario, et al.
Published: (2021)
by: González, Mario, et al.
Published: (2021)
Uncertainty-Aware Test-Time Adaptation for Inverse Consistent Diffeomorphic Lung Image Registration
by: Chaudhary, Muhammad F. A., et al.
Published: (2024)
by: Chaudhary, Muhammad F. A., et al.
Published: (2024)
Equivariant Test-Time Training with Operator Sketching for Imaging Inverse Problems
by: Xu, Guixian, et al.
Published: (2024)
by: Xu, Guixian, et al.
Published: (2024)
QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
by: Ye, Cassandra Tong, et al.
Published: (2025)
by: Ye, Cassandra Tong, et al.
Published: (2025)
Generalization in diffusion models arises from geometry-adaptive harmonic representations
by: Kadkhodaie, Zahra, et al.
Published: (2023)
by: Kadkhodaie, Zahra, et al.
Published: (2023)
CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
by: Djahnine, Aissam, et al.
Published: (2024)
by: Djahnine, Aissam, et al.
Published: (2024)
DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems
by: Ahamed, Shadab, et al.
Published: (2024)
by: Ahamed, Shadab, et al.
Published: (2024)
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems
by: Tang, Junqi, et al.
Published: (2022)
by: Tang, Junqi, et al.
Published: (2022)
MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model
by: Chand, Jyothi Rikhab, et al.
Published: (2025)
by: Chand, Jyothi Rikhab, et al.
Published: (2025)
Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
by: Yang, Gang
Published: (2025)
by: Yang, Gang
Published: (2025)
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
by: Tang, Haoyue, et al.
Published: (2024)
by: Tang, Haoyue, et al.
Published: (2024)
Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach
by: Chen, Haoxuan, et al.
Published: (2025)
by: Chen, Haoxuan, et al.
Published: (2025)
A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma
by: Wijethilake, Navodini, et al.
Published: (2023)
by: Wijethilake, Navodini, et al.
Published: (2023)
Solving Inverse Problems with FLAIR
by: Erbach, Julius, et al.
Published: (2025)
by: Erbach, Julius, et al.
Published: (2025)
Similar Items
-
Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser
by: Kadkhodaie, Zahra, et al.
Published: (2020) -
Learning a distance measure from the information-estimation geometry of data
by: Ohayon, Guy, et al.
Published: (2025) -
A polar prediction model for learning to represent visual transformations
by: Fiquet, Pierre-Étienne H., et al.
Published: (2023) -
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
by: Zhang, Ling-Qi, et al.
Published: (2024) -
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
by: Chen, Eric, et al.
Published: (2025)