Adaptive Optics Calibration for a Wide-Field Microscope

Adaptive optics calibration of a novel wide-field scanning microscope is described, comparing relevant parameters for several optimization techniques. Specifically, comparisons of the optimization algorithm, image quality metrics, and the calibration image target are detailed. It is shown that stochastic parallel gradient descent (SPGD) algorithm using image intensity as a metric provides robust, repeatable system optimization. Results also show that optimization performance improves when the feature sizes on the calibration target approach the diffraction limit and are more uniformly distributed. This paper further compares stochastic, image-based optimization performance to that of conventional adaptive optics optimization with a point source object and a Shack Hartmann wavefront sensor.