Paper
21 February 2017 Shot noise-limited Cramér-Rao bound and algorithmic sensitivity for wavelength shifting interferometry
Author Affiliations +
Proceedings Volume 10074, Quantitative Phase Imaging III; 100741G (2017) https://doi.org/10.1117/12.2254703
Event: SPIE BiOS, 2017, San Francisco, California, United States
Abstract
Sensitivity is a critical index to measure the temporal fluctuation of the retrieved optical pathlength in quantitative phase imaging system. However, an accurate and comprehensive analysis for sensitivity evaluation is still lacking in current literature. In particular, previous theoretical studies for fundamental sensitivity based on Gaussian noise models are not applicable to modern cameras and detectors, which are dominated by shot noise. In this paper, we derive two shot noiselimited theoretical sensitivities, Cramér-Rao bound and algorithmic sensitivity for wavelength shifting interferometry, which is a major category of on-axis interferometry techniques in quantitative phase imaging. Based on the derivations, we show that the shot noise-limited model permits accurate estimation of theoretical sensitivities directly from measured data. These results can provide important insights into fundamental constraints in system performance and can be used to guide system design and optimization. The same concepts can be generalized to other quantitative phase imaging techniques as well.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shichao Chen and Yizheng Zhu "Shot noise-limited Cramér-Rao bound and algorithmic sensitivity for wavelength shifting interferometry", Proc. SPIE 10074, Quantitative Phase Imaging III, 100741G (21 February 2017); https://doi.org/10.1117/12.2254703
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Interferometry

Detection and tracking algorithms

Phase imaging

Data modeling

Cameras

Electrons

Estimation theory

Back to Top