MFGAN : OCT image super-resolution and enhancement with blind degradation and multi-frame fusion

Abstract

Optical coherence tomography (OCT) is crucial in medical imaging, especially for retinal diagnostics. However, its effectiveness is often limited by imaging devices, resulting in high noise levels, low resolution, and reduced sampling rates, which hinder OCT image diagnosis. This paper proposes a generative adversarial network (GAN) based OCT image super-resolution framework that leverages a blind degradation and Multi-frame Fusion mechanism, namely MFGAN, for retinal OCT image super-resolution. Our method jointly performs denoising, blind super-resolution, and multi-frame fusion, which can reconstruct high quality OCT images without requiring paired ground-truth data. We employ a blind degradation model to handle OCT image degradation and a denoising prior to effectively process noisy inputs. Experimental results on the PKU37 dataset and the VIP Cup 2024 dataset demonstrate that MFGAN excels in both visual quality and quantitative performance, outperforming existing OCT image super-resolution methods.