PSF estimation by gradient descent fit to the ESF

Citation data:

Proceedings of SPIE - The International Society for Optical Engineering, ISSN: 0277-786X, Vol: 6059, Page: 60590E-60590E-9

Publication Year:
2006
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Repository URL:
https://scholarworks.boisestate.edu/electrical_facpubs/99
DOI:
10.1117/12.643071
Author(s):
Elisa H. Barney Smith; Luke C. Cui; Yoichi Miyake
Publisher(s):
SPIE-Intl Soc Optical Eng
Tags:
Materials Science; Physics and Astronomy; Computer Science; Mathematics; Engineering; spatial attribute characterization; point spread function; edge spread function; kurtosis factor; Electrical and Computer Engineering
conference paper description
Calibration of scanners and cameras usually involves measuring the point spread function (PSF). When edge data is used to measure the PSF, the differentiation step amplifies the noise. A parametric fit of the functional form of the edge spread function (ESF) directly to the measured edge data is proposed to eliminate this. Experiments used to test this method show that the Cauchy functional form fits better than the Gaussian or other forms tried. The effect of using a functional form of the PSF that differs from the true PSF is explored by considering bilevel images formed by thresholding. The amount of mismatch seen can be related to the difference between the respective kurtosis factors. © 2006 SPIE-IS&T.