Registration and restoration of Adaptive-Optics corrected retinal images

Raw individual Adaptive-Optics-corrected flood-illuminated retinal images are usually quite noisy because of safety flux limitations. These flood-illuminated images are also of poor contrast. Interpretation of such images is therefore difficult without an appropriate post-processing, which typically includes the registration of the recorded image stack into a mosaic image and the restoration of the latter.We have developed an image registration method in a MAP framework, based on previous work in astronomical imaging, and tailored for the specifics of retinal imaging, more precisely to the fact that the illumination of the retina and the transmission of the instrument is non-homogeneous, which makes conventional registration methods likely to fail. The mosaic image must then be deconvolved in order to visually restore the high-resolution brought by adaptive optics. To this aim, we perform an unsupervised myopic deconvolution that takes into account the 3D nature of the object being imaged. We successfully apply this whole processing chain to experimental in vivo images of retinal vessels.