Theory of multirate signal processing with application to signal and image reconstruction

Signal processing methods for signals sampled at di.erent rates are investigated and applied to the problem of signal and image reconstruction or superresolution reconstruction. The problem is approached from the viewpoint of linear mean-square estimation theory and multirate signal processing for o...

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Main Author: Scrofani, James W.
Other Authors: Therrien, Charles W.
Published: Monterey, California.: Naval Postgraduate School, 2005. 2012
Online Access:http://hdl.handle.net/10945/10049
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-100492014-11-27T16:08:45Z Theory of multirate signal processing with application to signal and image reconstruction Scrofani, James W. Therrien, Charles W. Department of Electrical and Computer Engineering. Signal processing methods for signals sampled at di.erent rates are investigated and applied to the problem of signal and image reconstruction or superresolution reconstruction. The problem is approached from the viewpoint of linear mean-square estimation theory and multirate signal processing for one-and twodimensional signals. A new look is taken at multirate system theory in one and two dimensions which provides the framework for these methodologies. A careful analysis of linear optimal .ltering for problems involving di.erent input and output sampling rates is conducted. This results in the development of index mapping techniques that simplify the formulation of Wiener-Hopf equations whose solution determine the optimal .lters. The required filters exhibit periodicity in both one and two dimensions, due to the difference in sampling rates. The reconstruction algorithms developed are applied to one-and two-dimensional reconstruction problems. 2012-08-22T15:31:02Z 2012-08-22T15:31:02Z 2005-09 http://hdl.handle.net/10945/10049 Approved for public release, distribution unlimited--Cover. Monterey, California.: Naval Postgraduate School, 2005.
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sources NDLTD
description Signal processing methods for signals sampled at di.erent rates are investigated and applied to the problem of signal and image reconstruction or superresolution reconstruction. The problem is approached from the viewpoint of linear mean-square estimation theory and multirate signal processing for one-and twodimensional signals. A new look is taken at multirate system theory in one and two dimensions which provides the framework for these methodologies. A careful analysis of linear optimal .ltering for problems involving di.erent input and output sampling rates is conducted. This results in the development of index mapping techniques that simplify the formulation of Wiener-Hopf equations whose solution determine the optimal .lters. The required filters exhibit periodicity in both one and two dimensions, due to the difference in sampling rates. The reconstruction algorithms developed are applied to one-and two-dimensional reconstruction problems.
author2 Therrien, Charles W.
author_facet Therrien, Charles W.
Scrofani, James W.
author Scrofani, James W.
spellingShingle Scrofani, James W.
Theory of multirate signal processing with application to signal and image reconstruction
author_sort Scrofani, James W.
title Theory of multirate signal processing with application to signal and image reconstruction
title_short Theory of multirate signal processing with application to signal and image reconstruction
title_full Theory of multirate signal processing with application to signal and image reconstruction
title_fullStr Theory of multirate signal processing with application to signal and image reconstruction
title_full_unstemmed Theory of multirate signal processing with application to signal and image reconstruction
title_sort theory of multirate signal processing with application to signal and image reconstruction
publisher Monterey, California.: Naval Postgraduate School, 2005.
publishDate 2012
url http://hdl.handle.net/10945/10049
work_keys_str_mv AT scrofanijamesw theoryofmultiratesignalprocessingwithapplicationtosignalandimagereconstruction
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