Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network

• URL: http://arxiv.org/abs/2303.01939v1
• Date: Fri, 3 Mar 2023 14:10:47 GMT
• Title: Retinal Image Restoration using Transformer and Cycle-Consistent Generative Adversarial Network
• Authors: Alnur Alimanov and Md Baharul Islam
• Abstract summary: Medical imaging plays a significant role in detecting and treating various diseases. We propose a retinal image enhancement method using a vision transformer and convolutional neural network.
• Score: 0.7868449549351486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical imaging plays a significant role in detecting and treating various diseases. However, these images often happen to be of too poor quality, leading to decreased efficiency, extra expenses, and even incorrect diagnoses. Therefore, we propose a retinal image enhancement method using a vision transformer and convolutional neural network. It builds a cycle-consistent generative adversarial network that relies on unpaired datasets. It consists of two generators that translate images from one domain to another (e.g., low- to high-quality and vice versa), playing an adversarial game with two discriminators. Generators produce indistinguishable images for discriminators that predict the original images from generated ones. Generators are a combination of vision transformer (ViT) encoder and convolutional neural network (CNN) decoder. Discriminators include traditional CNN encoders. The resulting improved images have been tested quantitatively using such evaluation metrics as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and qualitatively, i.e., vessel segmentation. The proposed method successfully reduces the adverse effects of blurring, noise, illumination disturbances, and color distortions while significantly preserving structural and color information. Experimental results show the superiority of the proposed method. Our testing PSNR is 31.138 dB for the first and 27.798 dB for the second dataset. Testing SSIM is 0.919 and 0.904, respectively.

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