  Ultra-high-definition (UHD) technology has attracted widespread attention due
to its exceptional visual quality, but it also poses new challenges for
low-light image enhancement (LLIE) techniques. UHD images inherently possess
high computational complexity, leading existing UHD LLIE methods to employ
high-magnification downsampling to reduce computational costs, which in turn
results in information loss. The wavelet transform not only allows downsampling
without loss of information, but also separates the image content from the
noise. It enables state space models (SSMs) to avoid being affected by noise
when modeling long sequences, thus making full use of the long-sequence
modeling capability of SSMs. On this basis, we propose Wave-Mamba, a novel
approach based on two pivotal insights derived from the wavelet domain: 1) most
of the content information of an image exists in the low-frequency component,
less in the high-frequency component. 2) The high-frequency component exerts a
minimal influence on the outcomes of low-light enhancement. Specifically, to
efficiently model global content information on UHD images, we proposed a
low-frequency state space block (LFSSBlock) by improving SSMs to focus on
restoring the information of low-frequency sub-bands. Moreover, we propose a
high-frequency enhance block (HFEBlock) for high-frequency sub-band
information, which uses the enhanced low-frequency information to correct the
high-frequency information and effectively restore the correct high-frequency
details. Through comprehensive evaluation, our method has demonstrated superior
performance, significantly outshining current leading techniques while
maintaining a more streamlined architecture. The code is available at
https://github.com/AlexZou14/Wave-Mamba.
