Image reconstruction enhanced by regularizers, eg, to enforce sparsity, low rank or
smoothness priors on images, has many successful applications in vision tasks such as …

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

Deep neural networks trained as image denoisers are widely used as priors for solving
imaging inverse problems. While Gaussian denoising is thought sufficient for learning image …

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 …

Estimating high-quality images while also quantifying their uncertainty are two desired
features in an image reconstruction algorithm for solving ill-posed inverse problems. In this …

Regularization by denoising (RED) framework has shown impressive performance for many
imaging inverse problems, by leveraging the denoising method in defining an explicit …

Quantitative acoustic microscopy (QAM) forms two-dimensional (2D) quantitative maps of
acoustic properties of thin tissue sections at a microscopic scale (m) using very-high …

La décomposition d'images numériques en d'autres bases ou dictionnaires que les
domaines temporel ou spatial est une approche très courante et efficace dans le traitement …

Decomposition of digital images into other basis or dictionaries than time or space domains
is a very common and effective approach in image processing and analysis. Such a …