A novel super-resolution image by curve fitting in the threshold decomposition domain

Abstract

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.

Date of Award	2008
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology