Semi-supervised generative adversarial learning for denoising adaptive optics retinal images.

Abstract

This study presents denoiseGAN, a novel semi-supervised generative adversarial network, for denoising adaptive optics (AO) retinal images. By leveraging both synthetic and real-world data, denoiseGAN effectively addresses various noise sources, including blur, motion artifacts, and electronic noise, commonly found in AO retinal imaging. Experimental results demonstrate that denoiseGAN outperforms traditional image denoising methods and the state-of-the-art conditional GAN model, preserving retinal cell structures and enhancing image contrast. Moreover, denoiseGAN aids downstream analysis, improving cell segmentation accuracy. Its 30% faster computational efficiency makes it a potential choice for real-time AO image processing in ophthalmology research and clinical practice.