Super-Resolução Regularizada e Simultânea de Seqüência de Imagens

Abstract

This work presents a new class of algorithms for super-resolution of images. In this new class of algorithms all frames of a sequence of images are simultaneously estimated by employing an iterative minimization of a regularized cost function. Similarly to other super-resolution techniques, the proposed approach exploits the existent correlation among the motion compensated frames. This correlated information helps to recover the details lost in the acquisition process and to obtain a image sequence with improved resolution. The proposed method achieved a better performance, when compared to other methods, by using the motion equations only in the prior term of the cost function. This original approach allows the production of an image sequence with higher fidelity and, at the same time, with lower computational complexity when compared with other algorithms in the literature. The isolation of the expressions that describes the correlation in the motion trajectory allows an increased control over the motion errors, and as a consequence, it was achieved an increased robustness. The performance of the proposed method were compared with other methods in the literature. In the comparative experiments it was considered the Euclidean norm, which is used to achieve low computational complexity, and the Huber norm, which is used to produce image with sharp edges and higher robustness to errors that occurs in the motion.