Plug-and-Play image restoration with Stochastic deNOising REgularization
This addresses a specific technical bottleneck in image restoration for researchers and practitioners, offering an incremental improvement over existing Plug-and-Play methods.
The paper tackles the mismatch in noise levels between denoiser training and application in Plug-and-Play image restoration by proposing SNORE, a framework that applies denoisers only on images with adequate noise levels, achieving competitive results on deblurring and inpainting tasks.
Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image inverse problems by combining a physical model and a deep neural network for regularization. Even if they produce impressive image restoration results, these algorithms rely on a non-standard use of a denoiser on images that are less and less noisy along the iterations, which contrasts with recent algorithms based on Diffusion Models (DM), where the denoiser is applied only on re-noised images. We propose a new PnP framework, called Stochastic deNOising REgularization (SNORE), which applies the denoiser only on images with noise of the adequate level. It is based on an explicit stochastic regularization, which leads to a stochastic gradient descent algorithm to solve ill-posed inverse problems. A convergence analysis of this algorithm and its annealing extension is provided. Experimentally, we prove that SNORE is competitive with respect to state-of-the-art methods on deblurring and inpainting tasks, both quantitatively and qualitatively.