Thesis Title: Analog Image Modeling Based Super Resolution and Application in Multi-spectral Imaging
Advisor: Dr. Weihong Guo
Abstract: Image super-resolution is an image reconstruction technique which attempts to reconstruct a high resolution image from one or more under-sampled low-resolution images of the same scene. High resolution images aid in analysis and inference in a multitude of digital imaging applications. However, due to limited accessibility to high-resolution imaging systems, a need arises for alternative measures to obtain the desired results. We propose a three-dimensional single image model to improve image resolution by estimating the analog image intensity function. We assume that the underlying analog image consists of smooth and edge components that can be approximated using a reproducible kernel Hilbert space function and the Heaviside function. We also extend the proposed method to pansharpening, a technology to fuse a high resolution panchromatic image with a low resolution multi-spectral image for a high resolution multi-spectral image. Various numerical results of the proposed formulation indicate competitive performance when compared to some state-of-the-art algorithms.