Myopic deconvolution of adaptive optics retina images

Adaptive Optics corrected flood imaging of the retina is a well-developed technique. The raw images are usually of poor contrast because they are dominated by an important background, and because AO correction is only partial. Interpretation of such images is difficult without an appropriate post-processing, typically background subtraction and image deconvolution. Deconvolution is difficult because the PSF is not well-known, which calls for myopic/blind deconvolution, and because the image contains in-focus and out-of-focus information from the object. In this communication, we tackle the deconvolution problem. We model the 3D imaging by assuming that the object is approximately the same in all planes within the depth of focus. The 3D model becomes a 2D model with the global PSF being an unknown linear combination of the PSF for each plane. The problem is to estimate the coefficients of this combination and the object. We show that the traditional method of joint estimation fails even for a small number of coefficients. We derive a marginal estimation of unknown hyperparameters (PSF coefficients, object Power Spectral Density and noise level) followed by a MAP estimation of the object. Such a marginal estimation has better statistical convergence properties, and allows us to obtain an "unsupervised" estimate of the object. Results on simulated and experimental data are shown.