SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising

Retinal fundus images are used for the diagnosis and treatment of various eye diseases such as diabetic retinopathy, glaucoma, exudates and so on. The retinal vasculature is difficult to investigate retinal conditions due to the presence of various noises in the retinal image during the capture of t...

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Veröffentlicht in:	IET image processing Jg. 13; H. 14; S. 2778 - 2789
Hauptverfasser:	Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 12.12.2019
Schlagworte:	biomedical optical imaging blood vessels convolution deep learning denoising effect denoising images diabetic retinopathy diseases eye eye diseases image denoising image representation image segmentation learning (artificial intelligence) medical image processing neural nets noise level noisy fundus novel stack deep convolutional autoencoder patched base training restoring features retinal conditions retinal fundus images retinal image denoising retinal vasculature Special Section: Adversarial Learning in Image Processing standard fundus databases target image visibility retinal image denoising retinal fundus images noisy fundus deep learning eye diseases image segmentation retinal conditions convolution noise level standard fundus databases learning (artificial intelligence) medical image processing denoising images visibility blood vessels restoring features diseases diabetic retinopathy image denoising target image eye denoising effect patched base training image representation novel stack deep convolutional autoencoder retinal vasculature neural nets biomedical optical imaging
ISSN:	1751-9659, 1751-9667
Online-Zugang:	Volltext
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