Fundus image quality serves a crucial asset for medical diagnosis and applications. However, such images often suffer degradation during image acquisition where multiple types of degradation can occur in each image. Although recent deep learning based methods have shown promising results in image enhancement, they tend to focus on restoring one aspect of degradation and lack generalisability to multiple modes of degradation. We propose an adaptive image enhancement network that can simultaneously handle a mixture of different degradations. The main contribution of this work is to introduce our Multi-Degradation-Adaptive module which dynamically generates filters for different types of degradation. Moreover, we explore degradation representation learning and propose the degradation representation network and Multi-Degradation-Adaptive discriminator for our accompanying image enhancement network. Experimental results demonstrate that our method outperforms several existing state-of-the-art methods in fundus image enhancement. Code will be available at https://github.com/RuoyuGuo/MDA-Net.

中文翻译：

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通过退化表示学习增强眼底图像的多退化适应网络


眼底图像质量是医学诊断和应用的重要资产。然而，这样的图像在图像采集期间经常遭受退化，其中每个图像中可能发生多种类型的退化。尽管最近基于深度学习的方法在图像增强方面显示出了有希望的结果，但它们往往侧重于恢复退化的某一方面，而缺乏对多种退化模式的通用性。我们提出了一种自适应图像增强网络，可以同时处理不同退化的混合。这项工作的主要贡献是介绍我们的多降级自适应模块，该模块可以动态生成针对不同类型降级的过滤器。此外，我们探索了退化表示学习，并为我们附带的图像增强网络提出了退化表示网络和多退化自适应判别器。实验结果表明，我们的方法在眼底图像增强方面优于几种现有的最先进的方法。代码可在 https://github.com/RuoyuGuo/MDA-Net 获取。