Experimental implementation of wavefront sensorless real-time adaptive optics aberration correction control loop with a neural network

Recently, deep neural network (DNN) based adaptive optics systems were proposed to address the issue of latency in existing wavefront sensorless (WFS-less) aberration correction techniques. Intensity images alone are sufficient for the DNN model to compute the necessary wavefront correction, removing the need for iterative processes and allowing practical real-time aberration correction to be implemented. Specifically, we generate the desired aberration correction phase profiles utilizing a DNN based system that outputs a set of coefficients for 27 terms of Zernike polynomials. We present an experimental realization of this technique using a spatial light modulator (SLM) on real physical turbulence-induced aberration. We report an aberration correction rate of 20 frames per second in this laboratory setting, accelerated by parallelization on a graphics processing unit. There are a number of issues associated with the practical implementation of such techniques, which we highlight and address in this paper.