Image metric-based multi-observation single-step deep deterministic policy gradient for sensorless adaptive optics

Abstract

Sensorless adaptive optics (SAO) has been widely used across diverse fields such as astronomy, microscopy, and ophthalmology. Recent advances have proved the feasibility of using the deep deterministic policy gradient (DDPG) for image metric-based SAO, achieving fast correction speeds compared to the coordinate search Zernike mode hill climbing (ZMHC) method. In this work, we present a multi-observation single-step DDPG (MOSS-DDPG) optimization framework for SAO on a confocal scanning laser ophthalmoscope (SLO) system with particular consideration for applications in preclinical retinal imaging. MOSS-DDPG optimizes N target Zernike coefficients in a single-step manner based on 2N + 1 observations of the image sharpness metric values. Through in silico simulations, MOSS-DDPG has demonstrated the capability to quickly achieve diffraction-limited resolution performance with long short-term memory (LSTM) network implementation. In situ tests suggest that knowledge learned through simulation adapts swiftly to imperfections in the real system by transfer learning, exhibiting comparable in situ performance to the ZMHC method with a greater than tenfold reduction in the required number of iterations.