Abstract
In this paper, we consider the image super-resolution (SR) reconstitution problem. The main goal consists of obtaining a high-resolution (HR) image from a set of low-resolution (LR) ones. For that, we propose a novel approach based on a regularized criterion. The criterion is composed of the classical generalized total variation (TV) but adding a bilateral filter (BTV) regularizer. The main goal of our approach consists of the derivation and the use of an efficient combined deblurring and denoising stage that is applied on the high-resolution image. We demonstrate the existence of minimizers of the combined variational problem in the bounded variation space, and we propose a minimization algorithm. The numerical results obtained by our approach are compared with the classical robust super-resolution (RSR) algorithm and the SR with TV regularization. They confirm that the proposed combined approach allows to overcome efficiently the blurring effect while removing the noise.
Highlights
The problem of the reconstruction of a super-resolution image from low-resolution ones is required in numerous applications such as video surveillance [1], medical diagnostics [2] and image satellite [3].A so-called fast robust super-resolution procedure was proposed in [4]
Our paper will focus on this second stage in the context of super resolution
We present in the following subsection the related work to the choice of the prior Gibbs function (PGF) function
Summary
The problem of the reconstruction of a super-resolution image from low-resolution ones is required in numerous applications such as video surveillance [1], medical diagnostics [2] and image satellite [3]. A so-called fast robust super-resolution procedure was proposed in [4]. In this approach, Farsiu et al proposed a two-stage approach. A high-resolution image is built, but having the problem of being blurred. The main goal consists of increasing the robustness of the super-resolution (SR) technique in [4] with respect to the blurring effect and to the noise. The problem of image deblurring or denoising is an ill-posed one. It is the main reason why the problem is considered as an optimization one, but considering a regularized criterion.
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