We introduce a novel generic energy functional that we employ to solve inverse imaging
problems within a variational framework. The proposed regularization family, termed as …

In this paper, we provide a short review of Retinex and then present a unifying framework.
The fundamental assumption of all Retinex models is that the observed image is a …

Even after two decades, the total variation (TV) remains one of the most popular
regularizations for image processing problems and has sparked a tremendous amount of …

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

Natural images exhibit geometric structures that are informative of the properties of the
underlying scene. Modern image processing algorithms respect such characteristics by …

We introduce a generic convex energy functional that is suitable for both grayscale and
vector-valued images. Our functional is based on the eigenvalues of the structure tensor …

Many analysis-based regularizations proposed so far employ a common prior information,
ie, edges in an image are sparse. However, in local edge regions and texture regions, this …

We propose a new space-variant anisotropic regularization term for variational image
restoration, based on the statistical assumption that the gradients of the target image …

Models related to the Euler's elastica energy have proven to be useful for many applications
including image processing. Extending elastica models to color images and multichannel …

The choice of a proper regularization measure plays an important role in the field of image
processing. One classical approach treats color images as two-dimensional surfaces …