Myopic deconvolution from wave-front sensing

Abstract

Deconvolution from wave-front sensing is a powerful and low-cost high-resolution imaging technique designed to compensate for the image degradation due to atmospheric turbulence. It is based on a simultaneous recording of short-exposure images and wave-front sensor (WFS) data. Conventional data processing consists of a sequential estimation of the wave fronts given the WFS data and then of the object given the reconstructed wave fronts and the images. However, the object estimation does not take into account the wave-front reconstruction errors. A joint estimation of the object and the respective wave fronts has therefore been proposed to overcome this limitation. The aim of our study is to derive and validate a robust joint estimation approach, called myopic deconvolution from wave-front sensing. Our estimator uses all data simultaneously in a coherent Bayesian framework. It takes into account the noise in the images and in the WFS measurements and the available a priori information on the object to be restored as well as on the wave fronts. Regarding the a priori information on the object, an edge-preserving prior is implemented and validated. This method is validated on simulations and on experimental astronomical data.