  Clinical screening with low-quality fundus images is challenging and
significantly leads to misdiagnosis. This paper addresses the issue of
improving the retinal image quality and vessel segmentation through retinal
image restoration. More specifically, a cycle-consistent generative adversarial
network (CycleGAN) with a convolution block attention module (CBAM) is used for
retinal image restoration. A modified UNet is used for retinal vessel
segmentation for the restored retinal images (CBAM-UNet). The proposed model
consists of two generators and two discriminators. Generators translate images
from one domain to another, i.e., from low to high quality and vice versa.
Discriminators classify generated and original images. The retinal vessel
segmentation model uses downsampling, bottlenecking, and upsampling layers to
generate segmented images. The CBAM has been used to enhance the feature
extraction of these models. The proposed method does not require paired image
datasets, which are challenging to produce. Instead, it uses unpaired data that
consists of low- and high-quality fundus images retrieved from publicly
available datasets. The restoration performance of the proposed method was
evaluated using full-reference evaluation metrics, e.g., peak signal-to-noise
ratio (PSNR) and structural similarity index measure (SSIM). The retinal vessel
segmentation performance was compared with the ground-truth fundus images. The
proposed method can significantly reduce the degradation effects caused by
out-of-focus blurring, color distortion, low, high, and uneven illumination.
Experimental results show the effectiveness of the proposed method for retinal
image restoration and vessel segmentation.
