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

Content Provider	IET Digital Library
Author	Ghosh, Swarup Kr Biswas, Biswajit Ghosh, Anupam
Abstract	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 the image. Removal of noise is an important aspect for better visibility and diagnosis of the noisy fundus in ophthalmology. This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder. The proposed scheme is implemented to restore the structural details of fundus as well as to decrease the noise level. Furthermore, the proposed model utilises shared layers with the optimal manner to reduce the noise level of the target image with minimal computational cost. To restore an image, the proposed model brings a patched base training on samples to suppress with one to one manner without any loss of information. To access the denoising effect of the proposed scheme, several standard fundus databases such as DRIVE, STARE and DIARETDB1 have been tested in this study. Comparing the efficiency of the suggested model with state-of-art methods, the proposed scheme gives better result in terms of qualitative and quantitative analysis.
Starting Page	2778
Ending Page	2789
Page Count	12
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6582
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Optical Imaging Blood Vessels Computer Vision And Image Processing Technique Convolution Deep Learning Denoising Effect Denoising Image Diabetic Retinopathy Diseases Eye Eye Diseases Image Denoising Image Representation Image Segmentation Knowledge Engineering Technique Learning in AI Medical Image Processing Neural Nets Noise Level Noisy Fundus Novel Stack Deep Convolutional Autoencoder Optical And Laser Radiation Optical, Image And Video Signal Processing Patched Base Training Patient Diagnostic Method And Instrumentation Restoring Feature Retinal Condition Retinal Fundus Image Retinal Image Denoising Retinal Vasculature Standard Fundus Database Target Image Visibility
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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