From the Gradient-Step Denoiser to the Proximal Denoiser and their associated convergent Plug-and-Play algorithms
This work addresses the challenge of implicit image priors in optimization for image processing, though it appears incremental as it builds on existing Plug-and-Play methods.
The paper tackles the problem of using denoisers as optimization operators in Plug-and-Play algorithms by analyzing the Gradient-Step Denoiser, which is trained to act as a gradient descent or proximity operator for an explicit functional while maintaining state-of-the-art denoising performance.
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.