Robust low-complexity algorithms for phase reconstruction
A robust and low-complexity algorithm is developed to infer the phase of an optical field from a sequence of intensity images since intensity measured under defocused condition contains information of phase. In previous model, the images are captured by moving camera along the optical axis. However,...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/10356/54502 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | A robust and low-complexity algorithm is developed to infer the phase of an optical field from a sequence of intensity images since intensity measured under defocused condition contains information of phase. In previous model, the images are captured by moving camera along the optical axis. However, it is impossible to recover the phase and intensity images of fast-moving object such as living cells or bubbles. In this paper, a new method to recover the phase based on Kalman filter is proposed using intensity images measured under different wavelengths of light source rather than moving the camera. Accuracy is found to be fundamentally related to defocus distance, wavelength difference and noise level. The method is applied and tested in real experiment. A set of color filters are utilized to get multiple wavelengths. Finally, a further improvement of phase retrieval targeting at low-frequency artifacts is introduced and applied to the proposed algorithm. The effectiveness of the method is demonstrated with comparisons of results and error analysis. Both simulated data and experimental data are used to demonstrate the improvement using nonlinear diffusion regularization. |
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