Iterative-trained semi-blind deconvolution algorithm to compensate straylight in retinal images

Ávila, Francisco J. (Universidad de Zaragoza) ; Ares, Jorge (Universidad de Zaragoza) ; Marcellán, María C. (Universidad de Zaragoza) ; Collados, María V. (Universidad de Zaragoza) ; Remón, Laura (Universidad de Zaragoza)
Iterative-trained semi-blind deconvolution algorithm to compensate straylight in retinal images
Resumen: The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor. The analysis of the point spread function of the optical system is an objective way to quantify the image degradation. In retinal imaging, the presence of corneal or cristalline lens opacifications spread the light at wide angular distributions. If the mathematical operator that degrades the image is known, the image can be restored through deconvolution methods. In the particular case of retinal imaging, this operator may be unknown (or partially) due to the presence of cataracts, corneal edema, or vitreous opacification. In those cases, blind deconvolution theory provides useful results to restore important spatial information of the image. In this work, a new semi-blind deconvolution method has been developed by training an iterative process with the Glare Spread Function kernel based on the Richardson-Lucy deconvolution algorithm to compensate a veiling glare effect in retinal images due to intraocular straylight. The method was first tested with simulated retinal images generated from a straylight eye model and applied to a real retinal image dataset composed of healthy subjects and patients with glaucoma and diabetic retinopathy. Results showed the capacity of the algorithm to detect and compensate the veiling glare degradation and improving the image sharpness up to 1000% in the case of healthy subjects and up to 700% in the pathological retinal images. This image quality improvement allows performing image segmentation processing with restored hidden spatial information after deconvolution.
Idioma: Inglés
DOI: 10.3390/jimaging7040073
Año: 2021
Publicado en: JOURNAL OF IMAGING 7, 4 (2021), [15 pp.]
ISSN: 2313-433X

Factor impacto CITESCORE: 4.8 - Engineering (Q2) - Medicine (Q2) - Computer Science (Q2)

Factor impacto SCIMAGO: 0.728 - Computer Graphics and Computer-Aided Design (Q2) - Radiology, Nuclear Medicine and Imaging (Q2) - Computer Vision and Pattern Recognition (Q2)

Tipo y forma: Article (Published version)
Área (Departamento): Área Óptica (Dpto. Física Aplicada)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Exportado de SIDERAL (2022-09-08-11:57:15)

Este artículo se encuentra en las siguientes colecciones:

 Record created 2021-08-20, last modified 2022-09-08

Versión publicada:
Rate this document:

Rate this document:
(Not yet reviewed)