Gabriel Dore

Abstract

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

Publication Date

2004

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

John Schott

Advisor/Committee Member

Robert Fiete

Advisor/Committee Member

Harvey Rhody

Comments

Physical copy available from RIT's Wallace Library at TA1637 .D67 2004

Campus

RIT – Main Campus

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