Spatially-adaptive regularized super-resolution image reconstruction using a gradient-based saliency measure

This paper addresses the super-resolution image reconstruction problem with the aim to produce a higher-resolution image based on its low-resolution counterparts. The proposed approach adaptively adjusts the degree of regularization using the saliency measure of the local content of the image. This is in contrast to that a spatially-invariant regularization is used for the whole image in conventional approaches. Furthermore, a gradient-based assessment criterion is proposed to measure the saliency of the image. Experiments are conducted to demonstrate the superior performance of the proposed approach.