Please use this identifier to cite or link to this item:
Title: Efficient Gaussian inference algorithms for phase imaging
Authors: Vazquez, Manuel A.
Zhong, Jingshan
Dauwels, Justin
Waller, Laura
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Abstract: Novel efficient algorithms are developed to infer the phase of a complex optical field from a sequence of intensity images taken at different defocus distances. The non-linear observation model is approximated by a linear model. The complex optical field is inferred by iterative Kalman smoothing in the Fourier domain: forward and backward sweeps of Kalman recursions are alternated, and in each such sweep, the approximate linear model is refined. By limiting the number of iterations, one can trade off accuracy vs. complexity. The complexity of each iteration in the proposed algorithm is in the order of N logN, where N is the number of pixels per image. The storage required scales linearly with N. In contrast, the complexity of existing phase inference algorithms scales with N3 and the required storage with N2. The proposed algorithms may enable real-time estimation of optical fields from noisy intensity images.
DOI: 10.1109/ICASSP.2012.6287959
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

Citations 50

Updated on Feb 2, 2023

Page view(s) 50

Updated on Feb 6, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.