Paper
17 February 2014 A novel error metric for parametric fitting of point spread functions
Author Affiliations +
Proceedings Volume 9029, Visual Information Processing and Communication V; 90290A (2014) https://doi.org/10.1117/12.2040602
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States
Abstract
Established work in the literature has demonstrated that with accurate knowledge of the corresponding blur kernel (or point spread function, PSF), an unblurred prior image can be reliably estimated from one or more blurred observations. It has also been demonstrated, however, that an incorrect PSF specification leads to inaccurate image restoration. In this paper, we present a novel metric which relates the discrepancy between a known PSF and a choice of approximate PSF, and the resulting effect that this discrepancy will have on the reconstruction of an unblurred image. Such a metric is essential to the accurate development and application of a parameterized PSF model.

Several error measures are proposed, which quantify the inaccuracy of image deblurring using a particular incorrect PSF. Using a set of simulation results, it is shown that the desired metric is feasible even without specification of the unblurred prior image or the radiometric response of the camera. It is also shown that the proposed metric accurately and reliably predicts the resulting deblurring error from the use of an approximate PSF in place of an exact PSF.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jonathan D. Simpkins and Robert L. Stevenson "A novel error metric for parametric fitting of point spread functions", Proc. SPIE 9029, Visual Information Processing and Communication V, 90290A (17 February 2014); https://doi.org/10.1117/12.2040602
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Cited by 1 scholarly publication.
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KEYWORDS
Point spread functions

Cameras

Image restoration

Device simulation

Digital image correlation

Digital imaging

Image sensors

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