  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.
