Unsupervised Super-Resolution of OCT Images Using Generative Adversarial Network for Improved Age-Related Macular Degeneration Diagnosis

Age-related macular degeneration (AMD) is the leading cause of progressive vision loss in the elderly. Optical coherence tomography (OCT) is a promising diagnostic tool for early detection and management of AMD. However, the speckle noise and low resolution (LR) of the OCT images affect its diagnostic viabilities. Therefore, denoising and super-resolution (SR) techniques present a potential solution to improve the quality of the OCT images. Recent methods rely on example-based approaches that require paired LR and high resolution (HR) images for training. However, the large scale acquisition of paired LR-HR images presents pertinent challenges in clinical settings. Therefore, this work proposes an unsupervised framework using the generative adversarial network (GAN) to perform fast and reliable SR without the requirement of aligned LR-HR pairs. We use adversarial learning with cycle consistency and identity mapping priors to preserve the spatial correlation, color and texture details in the generated clean HR images. Experimental results on clinical-grade OCT images show that the proposed method outperforms the existing methods both in terms of SR performance and computational time. Improved classification accuracy of 96.54% is obtained when the generated images are used for automated AMD diagnosis. The enhanced generalizability and faithful reconstruction attributes make the proposed method suitable for assisting ophthalmologists in better diagnosis and treatment planning.