Documents
Presentation Slides
Presentation Slides
Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
![](https://sigport.org/sites/default/files/styles/home/public/ICASSP_2023_0.jpg?itok=sQrNtdRB)
- DOI:
- 10.60864/m4s8-6b76
- Citation Author(s):
- Submitted by:
- Charles Laroche
- Last updated:
- 17 November 2023 - 12:07pm
- Document Type:
- Presentation Slides
- Document Year:
- 2023
- Event:
- Presenters:
- Charles Laroche
- Paper Code:
- SS-L12.1
- Categories:
- Log in to post comments
Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the convergence of the algorithm and provide results on restoration tasks such as super-resolution and deblurring with non-uniform blur.
Links: