From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms
By: Vincent Herfeld , Baudouin Denis de Senneville , Arthur Leclaire and more
Potential Business Impact:
Cleans up blurry pictures perfectly.
In this paper we analyze the Gradient-Step Denoiser and its usage in Plug-and-Play algorithms. The Plug-and-Play paradigm of optimization algorithms uses off the shelf denoisers to replace a proximity operator or a gradient descent operator of an image prior. Usually this image prior is implicit and cannot be expressed, but the Gradient-Step Denoiser is trained to be exactly the gradient descent operator or the proximity operator of an explicit functional while preserving state-of-the-art denoising capabilities.
Similar Papers
From Image Denoisers to Regularizing Imaging Inverse Problems: An Overview
Optimization and Control
Makes blurry pictures clear using smart computer tricks.
MAP Estimation with Denoisers: Convergence Rates and Guarantees
Machine Learning (CS)
Cleans up messy data to solve hard problems.
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction
Image and Video Processing
Makes MRI scans faster and clearer.