On learning and regularization in super-resolution imaging.

摘要

Advances in super-resolution imaging have been made by reconstruction, interpolation and example-based algorithmic techniques drawn from the fields of signal and image processing, machine learning, biologically-inspired computer vision, and psychology. However, the performance of super-resolution algorithms has been limited by constraints of sampling frequency, sensor dimensions, sensor noise, focus and motion blurring, and alignment between low-resolution input data samples. In this dissertation, we propose several techniques to improve the performance of state-of-the-art super-resolution techniques. Firstly, a concise introduction and literature survey of super-resolution imaging research is given. Secondly, novel dictionary learning techniques for super-resolution are presented. Thirdly, non-uniform image super-resolution over deformed image domains is approached using patch-redundancy as well as resolution-independence image models. Experimental results are good in visual quality and compare well with other state-of-the-art techniques. Future work should explore the extension of the proposed methods to video and stereoscopic imaging.