Abstract
Sensorless adaptive optics (AO) imaging systems have been widely studied in recent years. To reach optimum results, such systems require an efficient performance metric. In this paper, a new performance metric applied to sensorless AO system based on stochastic parallel gradient descent (SPGD) algorithm is presented. This new metric has better performance of stability and correction ability compared with other three performance metrics (i.e. Strehl Ratio, Power-In-Bucket and Image Sharpness), and similar with mean radius (MR) metric but easier to measure by a photo-detector using a mask which can be simply-manufactured. Numerical simulations of AO corrections of various random aberrations are performed. The results show the superiority of the new metric.
Sign-in/Register to access full text options 
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.
