000106712 001__ 106712
000106712 005__ 20220908120554.0
000106712 0247_ $$2doi$$a10.3390/jimaging7040073
000106712 0248_ $$2sideral$$a124612
000106712 037__ $$aART-2021-124612
000106712 041__ $$aeng
000106712 100__ $$0(orcid)0000-0002-9068-7728$$aÁvila, Francisco J.$$uUniversidad de Zaragoza
000106712 245__ $$aIterative-trained semi-blind deconvolution algorithm to compensate straylight in retinal images
000106712 260__ $$c2021
000106712 5060_ $$aAccess copy available to the general public$$fUnrestricted
000106712 5203_ $$aThe 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.
000106712 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000106712 592__ $$a0.728$$b2021
000106712 594__ $$a4.8$$b2021
000106712 593__ $$aComputer Graphics and Computer-Aided Design$$c2021$$dQ2
000106712 593__ $$aRadiology, Nuclear Medicine and Imaging$$c2021$$dQ2
000106712 593__ $$aComputer Vision and Pattern Recognition$$c2021$$dQ2
000106712 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000106712 700__ $$0(orcid)0000-0002-1124-0363$$aAres, Jorge$$uUniversidad de Zaragoza
000106712 700__ $$0(orcid)0000-0002-7516-3029$$aMarcellán, María C.$$uUniversidad de Zaragoza
000106712 700__ $$0(orcid)0000-0002-3299-253X$$aCollados, María V.$$uUniversidad de Zaragoza
000106712 700__ $$0(orcid)0000-0002-3979-4528$$aRemón, Laura$$uUniversidad de Zaragoza
000106712 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000106712 773__ $$g7, 4 (2021), [15 pp.]$$tJOURNAL OF IMAGING$$x2313-433X
000106712 8564_ $$s1252945$$uhttps://zaguan.unizar.es/record/106712/files/texto_completo.pdf$$yVersión publicada
000106712 8564_ $$s2771836$$uhttps://zaguan.unizar.es/record/106712/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000106712 909CO $$ooai:zaguan.unizar.es:106712$$particulos$$pdriver
000106712 951__ $$a2022-09-08-11:57:15
000106712 980__ $$aARTICLE