In multi-frame Super-Resolution (SR) image reconstruction a single High-Resolution (HR) image is created from a sequence of Low-Resolution (LR) frames. This work considers stochastic regularized multi-frame SR image reconstruction from the data-fidelity point of view. In fact, a novel estimator named  inv L1 norm is proposed for assuring fidelity to the measured data. This estimator presents t...

This paper presents a new approach to total variation (TV) based image deconvolution/deblurring, which is adaptive in the sense that it doesn’t require the user to specify the value of the regularization parameter. We follow the Bayesian approach of integrating out this parameter, which is achieved by using an approximation of the partition function of the probabilistic prior interpretation of ...

Owing to the edge preserving ability and low computational cost of the total variation (TV), variational models with the TV regularization have been widely investigated in the field of multiplicative noise removal. The key points of the successful application of these models lie in: the optimal selection of the regularization parameter which balances the data-fidelity term with the TV regulariz...

Image restoration aims to restore an image within a given domain from a blurred and noisy acquisition. However, the convolution operator, which models the degradation, is truncated in a real observation causing significant artifacts in the restored results. Typically, some assumptions are made about the boundary conditions (BCs) outside the field of view to reduce the ringing. We propose instea...

A major challenge in computed tomography imaging is to obtain high-quality images from low-dose measurements. Key to this goal are computationally efficient reconstruction algorithms combined with detailed signal models. We show that the recently introduced adaptive sparsifying transform (AST) signal model provides superior reconstructions from low-dose data at significantly lower cost than com...

In this paper, we address the issue of hyperspectral pansharpening, which consists in fusing a (low spatial resolution) hyperspectral image HX and a (high spatial resolution) panchromatic image P to obtain a high spatial resolution hyperspectral image. The problem is addressed under a convex variational constrained formulation. The fit-to-P data term favors high resolution hyperspectral images ...