  Low-light remote sensing images generally feature high resolution and high
spatial complexity, with continuously distributed surface features in space.
This continuity in scenes leads to extensive long-range correlations in spatial
domains within remote sensing images. Convolutional Neural Networks, which rely
on local correlations for long-distance modeling, struggle to establish
long-range correlations in such images. On the other hand, transformer-based
methods that focus on global information face high computational complexities
when processing high-resolution remote sensing images. From another
perspective, Fourier transform can compute global information without
introducing a large number of parameters, enabling the network to more
efficiently capture the overall image structure and establish long-range
correlations. Therefore, we propose a Dual-Domain Feature Fusion Network (DFFN)
for low-light remote sensing image enhancement. Specifically, this challenging
task of low-light enhancement is divided into two more manageable sub-tasks:
the first phase learns amplitude information to restore image brightness, and
the second phase learns phase information to refine details. To facilitate
information exchange between the two phases, we designed an information fusion
affine block that combines data from different phases and scales. Additionally,
we have constructed two dark light remote sensing datasets to address the
current lack of datasets in dark light remote sensing image enhancement.
Extensive evaluations show that our method outperforms existing
state-of-the-art methods. The code is available at
https://github.com/iijjlk/DFFN.
