Frequency Domain Decomposition Translation for Enhanced Medical Image Translation Using GANs

TL;DR

This paper introduces Frequency Domain Decomposition Translation (FDDT), a novel GAN-based method that decomposes images into high- and low-frequency components to improve medical image translation quality by preserving details and style.

Contribution

The paper proposes FDDT, a new frequency domain decomposition approach that enhances GAN-based image translation by better aligning frequency components, reducing distortion, and improving image quality.

Findings

FDDT reduces Fréchet inception distance by up to 24.4%.

FDDT improves structural similarity by up to 4.4%.

FDDT increases peak signal-to-noise ratio by up to 5.8%.

Abstract

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%.