Enhancing retinal images by extracting structural information

High-resolution imaging of the retina has significant importance for science: physics and optics, biology, and medicine. The enhancement of images with poor contrast and the detection of faint structures require objective methods for assessing perceptual image quality. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce a framework for quality assessment based on the degradation of structural information. We implemented a new processing technique on a long sequence of retinal images of subjects with normal vision. We were able to perform a precise shift-and-add at the sub-pixel level in order to resolve the structures of the size of single cells in the living human retina. Last, we quantified the restoration reliability of the distorted images using an improved quality assessment. To that purpose, we used the single image restoration method based on the ergodic principle, which has originated in solar astronomy, to deconvolve aberrations after adaptive optics compensation.