Frequency Domain Decomposition Translation for Enhanced Medical Image
  Translation Using GANs
Zhuhui Wang Jianwei Zuo Xuliang Deng Jiajia Luo
Medical Image-to-image translation is a key task in computer vision and
generative artificial intelligence, and it is highly applicable to medical
image analysis. GAN-based methods are the mainstream image translation methods,
but they often ignore the variation and distribution of images in the frequency
domain, or only take simple measures to align high-frequency information, which
can lead to distortion and low quality of the generated images. To solve these
problems, we propose a novel method called frequency domain decomposition
translation (FDDT). This method decomposes the original image into a
high-frequency component and a low-frequency component, with the high-frequency
component containing the details and identity information, and the
low-frequency component containing the style information. Next, the
high-frequency and low-frequency components of the transformed image are
aligned with the transformed results of the high-frequency and low-frequency
components of the original image in the same frequency band in the spatial
domain, thus preserving the identity information of the image while destroying
as little stylistic information of the image as possible. We conduct extensive
experiments on MRI images and natural images with FDDT and several mainstream
baseline models, and we use four evaluation metrics to assess the quality of
the generated images. Compared with the baseline models, optimally, FDDT can
reduce Fr\'echet inception distance by up to 24.4%, structural similarity by up
to 4.4%, peak signal-to-noise ratio by up to 5.8%, and mean squared error by up
to 31%. Compared with the previous method, optimally, FDDT can reduce Fr\'echet
inception distance by up to 23.7%, structural similarity by up to 1.8%, peak
signal-to-noise ratio by up to 6.8%, and mean squared error by up to 31.6%.