Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images
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 quanti...
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MDPI AG
2021-04-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/7/4/73 |
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| author | Francisco J. Ávila Jorge Ares María C. Marcellán María V. Collados Laura Remón |
| author_facet | Francisco J. Ávila Jorge Ares María C. Marcellán María V. Collados Laura Remón |
| author_sort | Francisco J. Ávila |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:15:06Z |
| format | Article |
| id | doaj.art-16a8ceb877dc4831ae7b925919060813 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T12:15:06Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-16a8ceb877dc4831ae7b9259190608132023-11-21T15:53:44ZengMDPI AGJournal of Imaging2313-433X2021-04-01747310.3390/jimaging7040073Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal ImagesFrancisco J. Ávila0Jorge Ares1María C. Marcellán2María V. Collados3Laura Remón4Departamento de Física Aplicada, Universidad de Zaragoza, 50009 Zaragoza, SpainDepartamento de Física Aplicada, Universidad de Zaragoza, 50009 Zaragoza, SpainDepartamento de Física Aplicada, Universidad de Zaragoza, 50009 Zaragoza, SpainDepartamento de Física Aplicada, Universidad de Zaragoza, 50009 Zaragoza, SpainDepartamento de Física Aplicada, Universidad de Zaragoza, 50009 Zaragoza, SpainThe 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.https://www.mdpi.com/2313-433X/7/4/73Richardson-Lucy deconvolutionblind deconvolutionintraocular straylightretinal imagingartificial intelligence |
| spellingShingle | Francisco J. Ávila Jorge Ares María C. Marcellán María V. Collados Laura Remón Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images Journal of Imaging Richardson-Lucy deconvolution blind deconvolution intraocular straylight retinal imaging artificial intelligence |
| title | Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images |
| title_full | Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images |
| title_fullStr | Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images |
| title_full_unstemmed | Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images |
| title_short | Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images |
| title_sort | iterative trained semi blind deconvolution algorithm to compensate straylight in retinal images |
| topic | Richardson-Lucy deconvolution blind deconvolution intraocular straylight retinal imaging artificial intelligence |
| url | https://www.mdpi.com/2313-433X/7/4/73 |
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