Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-transform-based domain generalization methods have gained popularity primarily because they exploit the power of Fourier transformation to capture essential patterns and regularities in the data, making the model more robust to domain shifts. The mainstream Fourier-transform-based domain generalization swaps the Fourier amplitude spectrum while preserving the phase spectrum between the source and the target images. However, it neglects background interference in the amplitude spectrum. To overcome this limitation, we introduce a soft-thresholding function in the Fourier domain. We apply this newly designed algorithm to retinal fundus image segmentation, which is important for diagnosing ocular diseases but the neural network's performance can degrade across different sources due to domain shifts. The proposed technique basically enhances fundus image augmentation by eliminating small values in the Fourier domain and providing better generalization. The innovative nature of the soft thresholding fused with Fourier-transform-based domain generalization improves neural network models' performance by reducing the target images' background interference significantly. Experiments on public data validate our approach's effectiveness over conventional and state-of-the-art methods with superior segmentation metrics.

翻译：域泛化旨在通过在多个源域上训练模型，使其能够良好地泛化至未见过的目标域。在众多域泛化方法中，基于傅里叶变换的方法尤为流行，主要因为它利用傅里叶变换的能力捕捉数据中的关键模式与规律，从而增强模型对域偏移的鲁棒性。当前主流的傅里叶变换域泛化方法通过交换源域与目标域图像的傅里叶幅度谱并保留相位谱来实现，但该方法忽略了幅度谱中的背景干扰。为克服这一局限，我们在傅里叶域中引入软阈值函数。我们将这一新设计的算法应用于视网膜眼底图像分割——该任务对诊断眼科疾病至关重要，但神经网络性能常因域偏移在不同数据源间下降。该技术通过消除傅里叶域中的小幅值分量并增强泛化能力，有效提升了眼底图像的数据增强效果。软阈值与基于傅里叶变换的域泛化方法的创新融合，通过显著降低目标图像的背景干扰，改善了神经网络模型的性能。在公开数据上的实验验证了该方法相比传统及最新技术的优越性，实现了更优的分割指标。