Name
Title | ||
|---|---|---|
Non-uniform sampling and Gaussian process regression in transport of intensity phase imaging. |
Abstract | ||
|---|---|---|
Gaussian process (GP) regression is a nonparametric regression method that can be used to predict continuous quantities. Here, we show that the same technique can be applied to a class of phase imaging techniques based on measurements of intensity at multiple propagation distances, i.e. the transport of intensity equation (TIE). In this paper, we demonstrate how to apply GP regression to estimate the first intensity derivative along the direction of propagation and incorporate nonuniform propagation distance sampling. The low-frequency artifacts that often occur in phase recovery using traditional methods can be significantly suppressed by the proposed GP TIE method. The method is shown to be stable with moderate amounts of Gaussian noise. We validate the method experimentally by recovering the phase of human cheek cells in a bright field microscope and show better performance as compared to other TIE reconstruction methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6855115 | ICASSP |
Keywords | DocType | ISSN |
Gaussian noise,biology computing,image reconstruction,image sampling,regression analysis,GP TIE method,GP regression,Gaussian noise,Gaussian process regression,TIE reconstruction methods,bright field microscope,human cheek cells,intensity measurements,intensity phase imaging techniques,low-frequency artifacts,multiple propagation distances,nonparametric regression method,nonuniform propagation distance sampling,nonuniform sampling,phase recovery,transport of intensity equation,Gaussian process,Phase imaging | Conference | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingshan Zhong | 1 | 14 | 2.16 |
| Rene A. Claus | 2 | 0 | 0.34 |
| Justin Dauwels | 3 | 57 | 11.60 |
| Lei Tian | 4 | 15 | 2.88 |
| Laura Waller | 5 | 0 | 1.01 |