A Contrast-aware Edge Enhancement GAN for Unpaired Anterior Segment OCT Image Denoising

Anterior segment optical coherence tomography (AS-OCT) is a popular imaging technique that can directly visualize the anterior segment structures while inherent speckle noise severely impairs visual readability and subsequent clinical analysis. Though unpaired OCT image denoising algorithms have been developed to improve visual quality considering the limited supervised clinical data, preserving the edge structures while denoising remains challenging, especially in AS-OCT images with little hierarchy and low contrast. This work proposes an edge enhancement generative adversarial network (E 2 GAN) based contrast-aware, particularly for unpaired AS-OCT image denoising. Specifically, to improve edge-structure consistency, we design a contrast attention mechanism for exploiting diverse hierarchical knowledge from multiple contrast images and adopt particular gradient-guided speckle filtering modules with an edge preservation loss for stabilizing the network. Additionally, considering that bi-directional GANs often focus on global appearance rather than essential features, E 2 GAN adds a perceptual quality constraint into the cycle consistency. Extensive experiments validate the superiority of E 2 GAN for AS-OCT image denoising and the benefits for downstream clinical analysis. Further experiments on the synthetic retinal OCT images prove the generalization of E 2 GAN.