We present a method for supervised learning of sparsity-promoting regularizers for image
denoising. Sparsity-promoting regularization is a key ingredient in solving modern image …

We present a gradient-based heuristic method for supervised learning of sparsity-promoting
regularizers for denoising signals and images. Sparsity-promoting regularization is a key …

In image denoising problems, one widely-adopted approach is to minimize a regularized
data-fit objective function, where the data-fit term is derived from a physical image …

Deep neural networks provide state-of-the-art performance for image denoising, where the
goal is to recover a near noise-free image from a noisy image. The underlying principle is …

We consider the problem of denoising with the help of prior information taken from a
database of clean signals or images. Denoising with variational methods is very efficient if a …

Abstract Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms
for image reconstruction that leverage the power of modern denoisers for image …

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-
convexity modulus. Such regularizers give rise to variational denoisers that minimize a …

Deep learning is an emerging approach for finding concise, slightly higher level
representations of the inputs, and has been successfully applied to many practical learning …

Image denoising can be described as the problem of mapping from a noisy image to a noise-
free image. The best currently available denoising methods approximate this mapping with …

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-
convexity modulus. Such regularizers give rise to variational denoisers that minimize a …