Abstract
The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis.
Highlights
Retinal imaging is one of the most useful modalities of clinical research and diagnosis in ophthalmology
We tested the proposed model on the synthetic retinal image dataset. This test was conducted on 50,000 adaptive optics (AO) retinal images of our test set, with the criteria of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and perceptual distance [37]
One should note that, compared with the images restored by DeblurGAN, SRNdeblur, and the proposed method, the image restored by augmented Lagrangian method (ALM) have the highest values of photoreceptor cell spatial frequencies when ranging from 50 to 75 c/deg
Summary
Retinal imaging is one of the most useful modalities of clinical research and diagnosis in ophthalmology. Direct observation of the retina is inevitably affected by ocular aberrations and the imaging resolution is severely limited. To compensate for the ocular aberrations, the adaptive optics (AO) technique was introduced to get nearly diffraction-limited resolution [1,2]. AO has helped us achieve microscopic imaging of the living human retina at the single-cell level and has been successfully integrated into the confocal scanning laser ophthalmoscope (SLO) to improve its imaging resolution of the human retina [3,4,5]. Affected by intraocular scatter, uncontrolled physiological vibration of eye, speckle noise, and other factors, the quality of adaptive optics confocal scanning laser ophthalmoscope (AOSLO) images is further degraded and the photoreceptor cells become obscured. An appropriate image post-processing method is indispensable to enhance the retinal images, which can help better detect the photoreceptor cells and assist clinicians in the examination of the human retina
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