Fast Single Image Learning-Based Super Resolution of Medical Images Using a New Analytical Solution for Reconstruction Problem

摘要

The process of retrieving images with high resolution using its low-resolution version is refereed to as super resolution. This paper proposes a fast and efficient algorithm that performs resolution enhancement and denoising of medical images. By using the patch pairs of high- and low-resolution images as database, the super-resolved image is recovered from their decimated, blurred and noise-added version. In this paper, the high-resolution patch to be estimated can be expressed as a sparse linear combination of HR patches over the database. Such linear combination of patches can be modelled as nonnegative quadratic problem. The computational cost of proposed method is reduced by finding closed form solution to the associated image reconstruction problem. Instead of traditional splitting strategy of decimation and convolution process, we decided to use the decimation and blurring operator's frequency domain properties simultaneously. Simulation result conducted on several images with various noise level shows the potency of our SR approach compared with existing super-resolution techniques.