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Found 9 papers, 1 papers with code
Overcoming Distribution Shifts in Plug-and-Play Methods with Test-Time Training
A common issue with the learned models is that of a performance drop when there is a distribution shift between the training and testing data.
PnP Restoration with Domain Adaptation for SANS
The prior in PR-SANS is initially trained on a set of generic images and subsequently fine-tuned using a limited amount of experimental SANS data.
Convergence of Nonconvex PnP-ADMM with MMSE Denoisers
This paper presents a theoretical explanation for the observed stability of PnP-ADMM based on the interpretation of the CNN prior as a minimum mean-squared error (MMSE) denoiser.
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input.
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Our second set of numerical results considers a simple and effective domain adaption strategy that closes the performance gap due to the use of mismatched denoisers.
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors.
DOLPH: Diffusion Models for Phase Retrieval
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements.
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
Online Deep Equilibrium Learning for Regularization by Denoising
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
