| Paper: |
BISP-P1.7 |
| Session: |
Biomedical Signal Processing I |
| Location: |
Poster Area H |
| Session Time: |
Tuesday, March 27, 14:00 - 16:00 |
| Presentation Time: |
Tuesday, March 27, 14:00 - 16:00 |
| Presentation: |
Poster |
| Topic: |
|
| Paper Title: |
Efficient Gaussian Inference Algorithms for Phase Imaging |
| Authors: |
Jingshan Zhong, Justin Dauwels, Nanyang Technological University, Singapore; Manuel Vazquez, Universidad Carlos III de Madrid, Spain; Laura Waller, Princeton University, United States |
| 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 N^3 and the required storage with N^2. The proposed algorithms may enable real-time estimation of optical fields from noisy intensity images. |