  Underwater images typically experience mixed degradations of brightness and
structure caused by the absorption and scattering of light by suspended
particles. To address this issue, we propose a Real-time Spatial and Frequency
Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors
and details in underwater images. Specifically, our proposed conditional
network is designed with Adaptive Fourier Gating Mechanism (AFGM) and
Multiscale Convolutional Attention Module (MCAM) to generate vectors carrying
low-frequency background information and high-frequency detail features, which
effectively promote the network to model global background information and
local texture details. To more precisely correct the color cast and low
saturation of the image, we introduce a Three-branch Feature Extraction (TFE)
block in the primary net that processes images pixel by pixel to integrate the
color information extended by the same channel (R, G, or B). This block
consists of three small branches, each of which has its own weights. Extensive
experiments demonstrate that our network significantly outperforms over
state-of-the-art methods in both visual quality and quantitative metrics.
