Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
