Unpaired Fundus Image Enhancement Using Image Decomposition

Abstract

Low-quality fundus images pose significant challenges for both ophthalmologists and computer-aided diagnosis systems. While many existing deep learning-based image quality enhancement algorithms require low- and high-quality image pairs for training, such pairs are often difficult to obtain in practice. On the other hand, unpaired image enhancement algorithms tend to struggle in preserving small structures and suppressing artefacts, which are crucial for medical applications. To address these issues, we propose an unpaired structure-preserving cycle quality alternating network for low-quality fundus image enhancement. Our method consists of three main components: (1) a cycle quality alternating framework to provide pixel-wise supervision for unpaired image enhancement, (2) a quality-aware disentangle module to enhance the extrinsic representation of the low-quality image with the high-quality reference image, and (3) an instance normalized skip to improve the network's structure-preserving capability. We tested our method on both synthetic and authentic clinical images with pathological structures and found it to be superior to state-of-the-art algorithms in terms of improving image quality while preserving delicate structures. Additionally, the proposed network demonstrated strong generalization ability in improving the quality of unseen images, as tested on 135-degree neonatal fundus images.