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

SUBMITTER: Xu G 

PROVIDER: S-EPMC11427189 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature