Research On Fast Image Super-Resolution Reconstruction Algorithms

Super-resolution image reconstruction is a technique which can obtain a high resolution image from one or multiple warped, blurred and noised low resolution image(s). It can increase the spatial resolution of image by software without updating hardware system. So it has a wide application in remote sensing, military detection, medical imaging, machine vision, public security, multimedia E-commerce and etc.This dissertation is concentrate on fast image SR algorithms. First, the matrix based image degrading model is obtained with the aid of matrix tensor product and the isomorphision that between matrix and vector space. Matrix model need less memory and analysis shows the saved memory is proportional to the product of subsampling factor and the dimension of low resolution image. Second, two fast SR algorithms called Matrix-Gradient-Descent and Matrix-Conjugate-Gradient which using the matrix as their variable are proposaled under the regular theory and matrix degrading model. Third, a projective operator is defined on the matrix space which can get the high resolution image from any other estimation directly. Combine with a Laplace high pass filter, high-frequency components can be got and boosted, this can be used to increase the spatial resolution of a image, based on this a fast iterative projection SR reconstruction algorithm is proposaled. At the end, SR software is designed to realize the proposed algorithms.The validity of proposaled algorithms is proved by computer simulation. And, low resolution image with large dimensions can be reconstuctioned directly by these algorithms for their faster speed. The iterative projection SR algorithm has the highest cost-performance when a trade off exists between the computing time and reconstruction quality.