Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN

Abstract

Speckle noise in optical coherence tomography (OCT) impairs both the visual quality and the performance of automatic analysis. Edge preservation is an important issue for speckle reduction. In this paper, we propose an end-to-end framework for simultaneous speckle reduction and contrast enhancement for retinal OCT images based on the conditional generative adversarial network (cGAN). The edge loss function is added to the final objective so that the model is sensitive to the edge-related details. We also propose a novel method for obtaining clean images for training from outputs of commercial OCT scanners. The results show that the overall denoising performance of the proposed method is better than other traditional methods and deep learning methods. The proposed model also has good generalization ability and is capable of despeckling different types of retinal OCT images.