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Deep-learning-based phase retrieval for pure phase objects in computational ghost imaging

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A deep-learning-based phase retrieval method is proposed for computational ghost imaging (CGI). In the method, a defocused intensity distribution of an object is obtained by the CGI. An object’s phase distribution is retrieved from the intensity distribution by a trained deep neural network. The advantage of the method is that the phase retrieval can be achieved with a low signal to noise ratio. The effects of noise level and defocus distance are investigated.

© 2018 Optical Society of Japan

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