Abstract
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.
Highlights
Detection of retinal pathologies such as glaucoma, age related macula degeneration (AMD) or retinitis pigmentosa (RP) is crucial in dealing with these conditions and calls for in vivo eye fundus imaging with a cellular level resolution
Once a specific complex imaging process is modeled and the training data is generated, the method can naturally handle this complex process which may be problematic for traditional deconvolution
The blind deconvolution process of adaptive optics (AO) retinal images is defined as the task of finding an original retinal image x, and possibly a residual point spread function (PSF) k, from an observed blurred image y which is created as y = x ∗ k + n, (1)
Summary
Detection of retinal pathologies such as glaucoma, age related macula degeneration (AMD) or retinitis pigmentosa (RP) is crucial in dealing with these conditions and calls for in vivo eye fundus imaging with a cellular level resolution. Direct observation of the retina suffers from various optical aberrations of the eye and the imaging resolution is severely limited. To compensate for ocular aberrations, adaptive optics (AO) technology was introduced and near diffraction-limited retinal images were achieved in 1997 [1]. The pollution caused by various sources of noise, such as stray light and CCD readout noise, further degrades the quality of retinal images [3]. Interpretation of such blurred retinal images is difficult for researchers or doctors and an appropriate image post-processing method is indispensable to make up for the limitation of AO retinal imaging procedure
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