Image-to-image translation has been revolutionized with GAN-based methods.
However, existing methods lack the ability to preserve the identity of the
source domain. As a result, synthesized images can often over-adapt to the
reference domain, losing important structural characteristics and suffering
from suboptimal visual quality. To solve these challenges, we propose a novel
frequency domain image translation (FDIT) framework, exploiting frequency
information for enhancing the image generation process. Our key idea is to
decompose the image into low-frequency and high-frequency components, where the
high-frequency feature captures object structure akin to the identity. Our
training objective facilitates the preservation of frequency information in
both pixel space and Fourier spectral space. We broadly evaluate FDIT across
five large-scale datasets and multiple tasks including image translation and
GAN inversion. Extensive experiments and ablations show that FDIT effectively
preserves the identity of the source image, and produces photo-realistic
images. FDIT establishes state-of-the-art performance, reducing the average FID
score by 5.6% compared to the previous best method.

该研究提出了一种基于频域的图像翻译框架（FDIT），能够保留源领域的标志性特征，生成视觉质量更优且类真实的图像。