Generalization of Iterative Restoration Techniques for Super-Resolution

The resolution enhancement of an image is always desirable, for almost all situations, but mainly if the image has the purpose of visual analysis. The hardware development for increasing the image resolution at its capture still has a higher cost than the algorithm solutions for super-resolution (SR). Like image restoration, super-resolution is also an ill-conditioned inverse problem. This work analyses the iterative restoration methods (Van Cittert, Tikhonov-Miller and Conjugate Gradient) which propose solutions for the ill-conditioning problem and compares them with the IBP method (Iterative Back Projection) proposed by Irani-Peleg [1] and Komatsu et al. [2]. The analysis of the found similarities is the basis of a generalization, that other iterative restoration methods can have their properties adapted, such 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.