Phase retrieval using regularization method in intensity correlation imaging

Xiyu Li; Xin Gao; Jia Tang; Changming Lu; Jianli Wang; Bin Wang

doi:10.1117/12.2072253

18 November 2014 Phase retrieval using regularization method in intensity correlation imaging

Xiyu Li, Xin Gao, Jia Tang, Changming Lu, Jianli Wang, Bin Wang

Proceedings Volume 9298, International Symposium on Optoelectronic Technology and Application 2014: Imaging Spectroscopy; and Telescopes and Large Optics; 92981G (2014) https://doi.org/10.1117/12.2072253
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

Intensity correlation imaging(ICI) method can obtain high resolution image with ground-based low precision mirrors, in the imaging process, phase retrieval algorithm should be used to reconstituted the object’s image. But the algorithm now used(such as hybrid input-output algorithm) is sensitive to noise and easy to stagnate. However the signal-to-noise ratio of intensity interferometry is low especially in imaging astronomical objects. In this paper, we build the mathematical model of phase retrieval and simplified it into a constrained optimization problem of a multi-dimensional function. New error function was designed by noise distribution and prior information using regularization method. The simulation results show that the regularization method can improve the performance of phase retrieval algorithm and get better image especially in low SNR condition

Citation Download Citation

Xiyu Li, Xin Gao, Jia Tang, Changming Lu, Jianli Wang, and Bin Wang "Phase retrieval using regularization method in intensity correlation imaging", Proc. SPIE 9298, International Symposium on Optoelectronic Technology and Application 2014: Imaging Spectroscopy; and Telescopes and Large Optics, 92981G (18 November 2014); https://doi.org/10.1117/12.2072253

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KEYWORDS

Image processing

Phase retrieval

Image quality

Image restoration

Signal to noise ratio

Image retrieval

Optimization (mathematics)

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