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Fidelity Analysis of Sampled Imaging SystemsMany modeling, simulation and performance analysis studies of sampled imaging systems are inherently incomplete because they are conditioned on a discrete-input, discrete-output model that only accounts for blurring during image acquisition and additive noise. For those sampled imaging systems where the effects of digital image acquisition, digital filtering and reconstruction are significant, the modeling, simulation and performance analysis should be based on a more comprehensive continuous-input, discrete-processing, continuous-output end-to-end model. This more comprehensive model should properly account for the low-pass filtering effects of image acquisition prior to sampling, the potentially important noiselike effects of the aliasing caused by sampling, additive noise due to device electronics and quantization, the generally high-boost filtering effects of digital processing, and the low-pass filtering effects of image reconstruction. This model should not, however, be so complex as to preclude significant mathematical analysis, particularly the mean-square (fidelity) type of analysis so common in linear system theory. We demonstrate that, although the mathematics of such a model is more complex, the increase in complexity is not so great as to prevent a complete fidelity-metric analysis at both the component level and at the end-to-end system level: that is, computable mean-square-based fidelity metrics are developed by which both component-level and system-level performance can be quantified. In addition, we demonstrate that system performance can be assessed qualitatively by visualizing the output image as the sum of three component images, each of which relates to a corresponding fidelity metric. The cascaded, or filtered, component accounts for the end-to-end system filtering of image acquisition, digital processing, and image reconstruction; the random noise component accounts for additive random noise, modulated by digital processing and image reconstruction filtering; and the aliased noise component accounts for the frequency folding effect of sampling, modulated by digital processing and image reconstruction filtering.
Document ID
20000063388
Acquisition Source
Langley Research Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Park, Stephen K.
(College of William and Mary Williamsburg, VA United States)
Rahman, Zia-ur
(College of William and Mary Williamsburg, VA United States)
Date Acquired
August 19, 2013
Publication Date
May 1, 1999
Publication Information
Publication: Optical Engineering
Publisher: Society of Photo-Optical Instrumentation Engineers
Volume: 38
Issue: 5
Subject Category
Computer Programming And Software
Funding Number(s)
CONTRACT_GRANT: NCC1-258
CONTRACT_GRANT: DAAL03-91-C-0034
Distribution Limits
Public
Copyright
Other

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