Super-resolution imaging via expectation-maximization estimation of near stellar neighborhoods

Stephen C. Cain

doi:10.1117/12.2319145

18 September 2018 Super-resolution imaging via expectation-maximization estimation of near stellar neighborhoods

Stephen C. Cain

Proceedings Volume 10772, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018; 107720E (2018) https://doi.org/10.1117/12.2319145
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Deconvolution techniques for removing the effects of diffraction from the Hubble space telescope have been employed for decades. This paper introduces a new solution for the deconvolution problem that separates the measurements into three sets of complete data. These data sets are a function of the unknown neighborhood around a star, the amplitude of the star that is known to exist and the background light and dark current measured during the acquisition process. In this paper the new method is tested with simulated Hubble space telescope data and compared to the traditional RichardsonLucy deconvolution algorithm. The results show that the new method can obtain imaging resolution well beyond the classical diffraction-limit and outperforms the Richardson-Lucy method by a substantial margin.

Conference Presentation

Citation Download Citation

Stephen C. Cain "Super-resolution imaging via expectation-maximization estimation of near stellar neighborhoods", Proc. SPIE 10772, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018, 107720E (18 September 2018); https://doi.org/10.1117/12.2319145

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