2013
Jingshan, Zhong; Dauwels, Justin; Vazquez, Manuel A; Waller, Laura
Sparse ACEKF for Phase Reconstruction. Artículo de revista
En: Optics express, vol. 21, no 15, pp. 18125–37, 2013, ISSN: 1094-4087.
Resumen | Enlaces | BibTeX | Etiquetas: Image reconstruction techniques, Phase retrieval
@article{Jingshan2013,
title = {Sparse ACEKF for Phase Reconstruction.},
author = {Zhong Jingshan and Justin Dauwels and Manuel A Vazquez and Laura Waller},
url = {http://www.opticsinfobase.org/viewmedia.cfm?uri=oe-21-15-18125\&seq=0\&html=true},
issn = {1094-4087},
year = {2013},
date = {2013-01-01},
journal = {Optics express},
volume = {21},
number = {15},
pages = {18125--37},
publisher = {Optical Society of America},
abstract = {We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.},
keywords = {Image reconstruction techniques, Phase retrieval},
pubstate = {published},
tppubtype = {article}
}
We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.