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In this work, we present a methodology for predicting the optical performance impacts of random and structured MSF surface errors using pupil-difference probability distribution (PDPD) moments. In addition, we show that, for random mid-spatial frequency (MSF) surface errors, performance estimates from the PDPD moments converge to performance estimates that assume random statistics. Finally, we apply these methods to several MSF surface errors with different distributions and compare estimated optical performance values to predictions based on earlier methods assuming random error distributions.
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Using the pupil difference probability density to understand OTF
We present the definition of a new quantity, the pupil difference probability density (PDPD), and describe its use in the study of imaging systems. Formally, the PDPD is defined as the probability density that two random points over the pupil, with given separation, have a given wavefront error difference. Under this definition, the PDPD is the one-dimensional Fourier transform, of the error difference variable, of the OTF. Using the PDPD, we show that it is possible to understand how certain sources of error affect the OTF. Further, given its geometric interpretation, this formalism is useful for finding accurate analytic approximations to the OTF.
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- PAR ID:
- 10097968
- Date Published:
- Journal Name:
- Computational Optics II
- Volume:
- 10694
- Page Range / eLocation ID:
- 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2316426
