Restoration of retinal images with space-variant blur

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. © 2014 Society of Photo-Optical Instrumentation Engineers.

• Fecha de publicación: 2014
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/9066
• Acceso en línea: https://hdl.handle.net/20.500.12585/9066
• Palabra clave: Blind deconvolution; Deblurring; Image restoration; Retinal image; Space-variant restoration; Blind deconvolution; Clinical resources; Deblurring; Innovative strategies; Point-spread functions; Restoration methods; Retinal image; Space variants; Aberrations; Convolution; Diagnosis; Image reconstruction; Ophthalmology; Restoration; Image enhancement; Algorithm; Angiography; Article; Artifact; Astigmatism; Automated pattern recognition; Eye fundus; Human; Image processing; Methodology; Normal distribution; Optics; Pathology; Reproducibility; Retina; Retina blood vessel; Statistical model; Vision; Visual system examination; Algorithms; Angiography; Artifacts; Astigmatism; Diagnostic Techniques, Ophthalmological; Fundus Oculi; Humans; Image Processing, Computer-Assisted; Models, Statistical; Normal distribution; Optics and Photonics; Pattern Recognition, Automated; Reproducibility of Results; Retina; Retinal Vessels; Vision, Ocular
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/