The richness of natural images makes the quest for optimal representations in image
processing and computer vision challenging. The latter observation has not prevented the …

This paper introduces a novel framework for the study of the total-variation model for image
denoising. In the model, the denoised image is the proximity operator of the total-variation …

The low-rank matrix approximation (LRMA) is an efficient image denoising method to reduce
additive Gaussian noise. However, the existing low-rank matrix approximation does not …

We consider the scenario where additive, independent, and identically distributed (iid) noise
in an image is removed using an overcomplete set of linear transforms and thresholding …

The deep unfolding approach has attracted significant attention in computer vision tasks,
which well connects conventional image processing modeling manners with more recent …

Since their introduction in the pioneering work by Schoenberg [75], splines have become
one of the powerful tools in Mathematics [2, 47, 76, 77, 95] and in computer-aided geometric …

This paper studies a problem of image restoration that observed images are contaminated
by Gaussian and impulse noise. Existing methods for this problem in the literature are based …

We investigate image and video denoising using adaptive dual-tree discrete wavelet
packets (ADDWP), which is extended from the dual-tree discrete wavelet transform (DDWT) …

In this paper, we propose a new framework for solving state estimation problems with an
additional sparsity-promoting L 1-regularizer term. We first formulate such problems as …

In infrared astronomy, an observed image from a chop-and-nod process can be considered
as the result of passing the original image through a high-pass filter. Here we propose a …