Online Regularization by Denoising with Applications to Phase Retrieval
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.
Cite
Text
Wu et al. "Online Regularization by Denoising with Applications to Phase Retrieval." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00482Markdown
[Wu et al. "Online Regularization by Denoising with Applications to Phase Retrieval." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/wu2019iccvw-online/) doi:10.1109/ICCVW.2019.00482BibTeX
@inproceedings{wu2019iccvw-online,
title = {{Online Regularization by Denoising with Applications to Phase Retrieval}},
author = {Wu, Zihui and Sun, Yu and Liu, Jiaming and Kamilov, Ulugbek},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {3887-3895},
doi = {10.1109/ICCVW.2019.00482},
url = {https://mlanthology.org/iccvw/2019/wu2019iccvw-online/}
}