Image super-resolution by curve fitting in the threshold decomposition domain

A new curve-fitting scheme is proposed in this paper to produce super-resolution images from a single low-resolution source image. The most unique feature of this method is that the threshold decomposition is performed on the given source image to obtain multiple binary images so that the curve-fitting applied on each resulted binary image can be made very efficient and accurate, thus allowing us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large up-scaling factor is used. Two novel techniques are further proposed to improve the visual quality: (1) a spreading technique (applied on some significant pixels detected in each threshold decomposed binary image) is used to remove ladder-like false edges that often appear visually in super-resolution images, and (2) an edge correction (guided by the edge information extracted from the original source image) is used to sharpen all inherent edges. Our results are compared with those achieved by using the state-of-arts techniques, showing the ability of our algorithm to achieve a better visual quality in smooth areas as well as for sharp edges and small objects.

► We introduce a new curve-fitting scheme to generate super-resolution images.
► We apply the threshold decomposition on the given low-resolution source image.
► Curve-fitting on each binary image becomes very efficient and accurate.
► Tiny objects and thin structures end up with a rather nice visual result even under a large up-scaling factor.