Feature issue introduction: applications of adaptive optics.

This feature issue of Optics Express follows the 2020 Imaging and Applied Optics Congress and comprises of articles on the development and use of adaptive optics across the broad range of domains in which the technique has been applied - including atmospheric correction, ophthalmology, vision science, microscopy, optical communications and beam control. This review provides a basic introduction to adaptive optics and a summary of the multidisciplinary articles included in this issue.

Statistics of intensity in adaptive-optics images and their usefulness for detection and photometry of exoplanets.

This paper is an introduction to the problem of modeling the probability density function of adaptive-optics speckle. We show that with the modified Rician distribution one cannot describe the statistics of light on axis. A dual solution is proposed: the modified Rician distribution for off-axis speckle and gamma-based distribution for the core of the point spread function. From these two distributions we derive optimal statistical discriminators between real sources and quasi-static speckles. In the second part of the paper the morphological difference between the two probability density functions is used to constrain a one-dimensional, "blind," iterative deconvolution at the position of an exoplanet. Separation of the probability density functions of signal and speckle yields accurate differential photometry in our simulations of the SPHERE planet finder instrument.

Deconvolution of adaptive optics retinal images.

We quantitatively demonstrate the improvement to adaptively corrected retinal images by using deconvolution to remove the residual wave-front aberrations. Qualitatively, deconvolution improves the contrast of the adaptive optics images. In this work we demonstrate that quantitative information is also increased by investigation of the improvement to cone classification due to the reduction in confusion of adjacent cones because of the extended wings of the point-spread function. The results show that the error in classification between the L and M cones is reduced by a factor of 2, thereby reducing the number of images required by a factor of 4.