A new performance metric on sensorless adaptive optics imaging system

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.