Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use.

Academy of Sciences of the Czech Republic Institute of Information Theory and Automation Pod Vodárenskou veží 4 18208 Prague 8 Czech Republic

Universitat Politècnica de Catalunya Department of Optics and Optometry Group of Applied Optics and Image Processing Violinista Vellsolà 37 08222 Terrassa Spain

Universitat Politècnica de Catalunya Department of Optics and Optometry Group of Applied Optics and Image Processing Violinista Vellsolà 37 08222 Terrassa SpainbUniversidad Tecnológica de Bolívar Facultad de Ciencias Básicas Km 1 vía Turbaco Carta

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