Misalignment-Robust Frequency Distribution Loss for Image Transformation
Zhangkai Ni Juncheng Wu Zian Wang Wenhan Yang Hanli Wang Lin Ma
This paper aims to address a common challenge in deep learning-based image
transformation methods, such as image enhancement and super-resolution, which
heavily rely on precisely aligned paired datasets with pixel-level alignments.
However, creating precisely aligned paired images presents significant
challenges and hinders the advancement of methods trained on such data. To
overcome this challenge, this paper introduces a novel and simple Frequency
Distribution Loss (FDL) for computing distribution distance within the
frequency domain. Specifically, we transform image features into the frequency
domain using Discrete Fourier Transformation (DFT). Subsequently, frequency
components (amplitude and phase) are processed separately to form the FDL loss
function. Our method is empirically proven effective as a training constraint
due to the thoughtful utilization of global information in the frequency
domain. Extensive experimental evaluations, focusing on image enhancement and
super-resolution tasks, demonstrate that FDL outperforms existing
misalignment-robust loss functions. Furthermore, we explore the potential of
our FDL for image style transfer that relies solely on completely misaligned
data. Our code is available at: https://github.com/eezkni/FDL