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.

翻译：水下图像通常会由于悬浮颗粒对光的吸收和散射而遭受亮度和结构的混合退化。为解决这一问题，我们提出了一种用于高效增强水下图像色彩和细节的实时空间与频域调制网络（RSFDM-Net）。具体而言，我们设计的条件网络采用自适应傅里叶门控机制（AFGM）和多尺度卷积注意力模块（MCAM），生成携带低频背景信息和高频细节特征的向量，从而有效促进网络对全局背景信息和局部纹理细节的建模。为了更精确地校正图像的颜色偏差和低饱和度，我们在主网络中引入了三分支特征提取（TFE）模块，该模块逐像素处理图像，以整合相同通道（R、G或B）扩展的色彩信息。该模块由三个小型分支组成，每个分支拥有独立的权重。大量实验表明，我们的网络在视觉质量和量化指标上均显著优于现有最先进方法。