GAN-based super-resolution for confocal superficial eyelid imaging: Real-time, domain generalization, and noise robustness

Objective: Infection by Demodex mites is a significant factor in causing blepharitis. The collected superficial eyelid tissue images currently have low resolution and strong noise. High-quality reconstruction of low-quality images is needed to assist ophthalmologists in accurately and quickly diagnosing mite infestations. Methods: We propose a novel GAN-based super-resolution reconstruction model. We utilize Residual-in Residual Dense Blocks (RRDB) as the fundamental building units and employ high-order degradation to simulate real-world degradation. Additionally, we introduce a new attention mechanism, All Attention Mechanism (AAM), which effectively captures and utilizes crucial image features, expanding the scope of utilized pixels. This significantly enhances the quality of reconstructed images. Results: Compared to existing super-resolution reconstruction models, our model demonstrates the strongest generalization ability and robustness. On the CIDMG and FMD datasets, we achieve the lowest NIQE and the highest SSIM values. Our model outperforms the SOTA SwinIR model, reducing reconstruction time by 76% and improving the image quality by 6.6% for low-noise images and 15.3% for high-noise images. Conclusion: By combining GAN with AAM, we have significantly improved the model's generalization ability, robustness, and real-time performance. Significance: This is the first time that domain generalization has been used for reconstructing superficial eyelid tissue images. The stable and fast reconstruction performance reduces the impact of noise and low resolution on image quality, significantly reducing examination time and improving diagnostic accuracy. This greatly enhances the clinical utility of confocal microscopy for eyelid demodex examination.

Automated summary

This research proposes a GAN-based super-resolution reconstruction model which significantly enhances the quality of eyelid tissue images by introducing a new attention mechanism and high-order degradation simulation. Compared to existing models, the proposed model demonstrates the strongest generalization ability and robustness, reducing reconstruction time and improving image quality for low and high-noise images.