Online Regularization by Denoising with Applications to Phase Retrieval
arXiv:1909.02040
Abstract
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems. Most RED algorithms are iterative batch procedures, which limits their applicability to very large datasets. In this paper, we address this limitation by introducing a novel online RED (On-RED) algorithm, which processes a small subset of the data at a time. We establish the theoretical convergence of On-RED in convex settings and empirically discuss its effectiveness in non-convex ones by illustrating its applicability to phase retrieval. Our results suggest that On-RED is an effective alternative to the traditional RED algorithms when dealing with large datasets.
arXiv commentAccepted ICCVW 2019 (LCI)
References in corpus (5)
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Fast Image Recovery Using Variable Splitting and Constrained Optimization
- An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
- Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
- Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm