Adaptive optics images joint deconvolution based on power spectra density of object and PSF

The atmospheric turbulence severely limits the angular resolution of ground based telescopes. When using Adaptive Optics (AO) compensation, the wavefront sensor data permit the estimation of the residual PSF. Yet, this estimation is imperfect, and a deconvolution is required for reaching the diffraction limit. A joint deconvolution method based on power spectra density (PSD) for AO image is presented. It deduces from a Bayesian framework in the context of imaging through turbulence with adaptive optics. This method uses a noise model that accounts for photonic and detector noises. It incorporates a positivity constraint and some a priori knowledge of the object (an estimate of its local mean and a model for its power spectral density). Finally, it reckons with an imperfect knowledge of the point spread function (PSF) by estimating the PSF jointly with the object under soft constraints rather than blindly. These constraints are designed to embody our knowledge of the PSF. Deconvolution results are presented for both simulated and experimental data.