Sparse ACEKF for phase reconstruction.
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.
Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798. zhongjingshan@hotmail.com
Zhong
Z
Jingshan
Justin
J
Dauwels
Manuel A
MA
Vázquez
Laura
L
Waller
D000465:Algorithms; D016477:Artifacts; D003627:Data Interpretation, Statistical; D005246:Feedback; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio