Image enhancement is an essential technique used in many imaging applications. The main
motivation of image enhancement is processing an image to be more suitable for specific …

Image denoising models based on the total variation (TV) regularization have been used in
many fields of image processing. The main advantage of the TV regularization can preserve …

Over the last 30 years a plethora of variational regularization models for image
reconstruction have been proposed and thoroughly inspected by the applied mathematics …

Image dehazing is a traditional task, yet it still presents arduous problems, especially in the
removal of haze from the texture and edge information of an image. The state-of-the-art …

In this paper, we propose an improved enhancement space-variant anisotropic PDE-
constrained for image denoising, based on a learning optimization procedure. Since the …

We propose an efficient estimation technique for the automatic selection of locally-adaptive
Total Variation regularisation parameters based on an hybrid strategy which combines a …

Recently, we have witnessed the success of total variation (TV) for many imaging
applications. However, traditional TV is defined on the original pixel domain, which limits its …

In the last years, the design of image reconstruction methods in the field of quantitative
Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when …

Recovering temporal image sequences (videos) based on indirect, noisy, or incomplete data
is an essential yet challenging task. We specifically consider the case where each data set is …

In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-
induced minimization scheme. The model is derived from the observations that semi-sparsity …