Super resolução baseada em métodos iterativos de restauração

Abstract

The resolution enhancement of an image is always desirable, independently of its objective, but mainly if the image has the purpose of visual analysis. The hardware development for increasing the image resolution still has a higher cost than the algorithmic solutions for super-resolution. Like image restoration, super-resolution is also an ill-conditioned inverse problem, and has an infinite number of solutions. This work analyzes the iterative restoration methods (Van Cittert, Tikhonov-Miller and Conjugate Gradiente) which propose solutions for the ill-conditioning problem and compares them with the IBP method (Iterative Back Projection). The analysis of the found similarities is the basis of a generalization, such that other iterative restoration methods can have their properties adapted, as regularization of the ill-conditioning, noise reduction and other degradations and the increase of the convergence rate can be incorporated to the techniques of super-resolution. Two new methods were created as case studies of the proposed generalization: the first one is a super-resolution method for dynamic magnetic resonance imaging (MRI) of the swallowing process, that uses an adaptiveWiener filtering as regularization and a non-rigid registration; and the second one is a pan sharpening method of SPOT satellite bands, that uses sampling based on sensor s characteristics and non-adaptive Wiener filtering.