Open AccessDeblurring adaptive optics retinal images using deep convolutional neural networks
Author(s) -
Fei Xiao,
Junlei Zhao,
Haoxin Zhao,
Yun Dai,
Yudong Zhang
Publication year - 2017
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.8.005675
Subject(s) - computer science , deblurring , artificial intelligence , retinal , adaptive optics , convolutional neural network , image quality , computer vision , preprocessor , image processing , pattern recognition (psychology) , optics , image restoration , image (mathematics) , ophthalmology , physics , medicine
The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved.