A curve-fitting scheme is proposed in this thesis to produce super-resolution images from a given low-resolution source image. The novelty of this method lies on the fact that the threshold decomposition is applied on the source image to obtain multiple binary images so that curve-fitting applied on each resulted binary image becomes very efficient - which allows us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large 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 ed...[ Read more ]

A curve-fitting scheme is proposed in this thesis to produce super-resolution images from a given low-resolution source image. The novelty of this method lies on the fact that the threshold decomposition is applied on the source image to obtain multiple binary images so that curve-fitting applied on each resulted binary image becomes very efficient - which allows us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large 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 low-resolution 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.