  Recently, Fourier frequency information has attracted much attention in
Low-Light Image Enhancement (LLIE). Some researchers noticed that, in the
Fourier space, the lightness degradation mainly exists in the amplitude
component and the rest exists in the phase component. By incorporating both the
Fourier frequency and the spatial information, these researchers proposed
remarkable solutions for LLIE. In this work, we further explore the positive
correlation between the magnitude of amplitude and the magnitude of lightness,
which can be effectively leveraged to improve the lightness of low-light images
in the Fourier space. Moreover, we find that the Fourier transform can extract
the global information of the image, and does not introduce massive neural
network parameters like Multi-Layer Perceptrons (MLPs) or Transformer. To this
end, a two-stage Fourier-based LLIE network (FourLLIE) is proposed. In the
first stage, we improve the lightness of low-light images by estimating the
amplitude transform map in the Fourier space. In the second stage, we introduce
the Signal-to-Noise-Ratio (SNR) map to provide the prior for integrating the
global Fourier frequency and the local spatial information, which recovers
image details in the spatial space. With this ingenious design, FourLLIE
outperforms the existing state-of-the-art (SOTA) LLIE methods on four
representative datasets while maintaining good model efficiency.
