The development of efficient software tools capable of super- resolving multi-spectral image sequences on-the-fly is an important step toward the production of imaging systems capable of acquiring vital imagery of hostile environments at an affordable price. A number of image processing tools already available for use in target recognition and identification rely on the availability of high-resolution imagery which cannot be safely acquired at a reasonable price. This thesis investigates the use of multiframe super-resolution as a tool to increase the spatial resolution of image sequences acquired with sensors commonly used in consumer video cameras. Multiframe super-resolution is the branch of imaging science which tries to restore high-resolution estimates of a scene utilizing a sequence of under-sampled images of that scene. Although a number of algorithms have already been developed to deal with this problem, they have unfortunately not been extended to deal with multi-spectral images acquired from moving imaging platforms. This thesis performs such extension for one of the most successful super-resolution algorithm and demonstrates that it can be used to improve the performance of common multi-spectral imaging systems utilizing Color Filter Arrays to acquire spectral data.
Library of Congress Subject Headings
Image processing--Digital techniques; Resolution (Optics); Remote-sensing images; Imaging systems; Algorithms
Imaging Science (MS)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Dore, Gabriel, "Multiframe Super-Resolution of Color Image Sequences Using a Global Motion Model" (2004). Thesis. Rochester Institute of Technology. Accessed from
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