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Superresolution in Image Sequences

The term “superresolution” refers to the process of obtaining higher-resolution images from several lower-resolution ones, i.e. resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. Superresolution allows to overcome the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This thesis presents a unified matrix-based framework that formulates superresolution as an inverse problem. We show explicitly how to construct every matrix involved in the formulation, and reduce the problem to a single matrix equation. The solution involves matrix inversion via Tikhonov regularization. It is tested on synthetic data, demonstrating the feasibility of the approach, and then results using experimental data are presented. We also investigate the problem of image registration, describing a procedure to measure the shift between images with subpixel accuracy. The results of this work can be used for object tracking and identification.