Fidelity analysis of sampled imaging systems
Abstract
Many modeling, simulation and performance analysis studies of sampled imaging systems are inherently incomplete because they are conditioned on a discreteinput, discreteoutput 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 continuousinput, discrete processing, continuousoutput endtoend model. This more comprehensive model should properly account for the lowpass 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 highboost filtering effects of digital processing, and the lowpass filtering effects of image reconstruction. This model should not, however, be so complex as to preclude significant mathematical analysis, particularly the meansquare (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 fidelitymetric analysis at both the component level and at the endtoend system level; that is, computable meansquarebased fidelity metrics are developed by which both componentlevel and systemlevel performance can be quantified. In addition, we demonstrate that system performance can be assessed qualitatively by visualizing the output images as the sum of three component images, each of which relates to a corresponding fidelity metric. The cascaded, or filtered, component accounts for the endtoend 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.
 Publication:

Optical Engineering
 Pub Date:
 May 1999
 DOI:
 10.1117/1.602047
 Bibcode:
 1999OptEn..38..786P