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Underwater images typically experience mixed degradations of brightness andstructure caused by the absorption and scattering of light by suspendedparticles. To address this issue, we propose a Real-time Spatial and FrequencyDomains Modulation Network (RSFDM-Net) for the efficient enhancement of colorsand details in underwater images. Specifically, our proposed conditionalnetwork is designed with Adaptive Fourier Gating Mechanism (AFGM) andMultiscale Convolutional Attention Module (MCAM) to generate vectors carryinglow-frequency background information and high-frequency detail features, whicheffectively promote the network to model global background information andlocal texture details. To more precisely correct the color cast and lowsaturation of the image, we introduce a Three-branch Feature Extraction (TFE)block in the primary net that processes images pixel by pixel to integrate thecolor information extended by the same channel (R, G, or B). This blockconsists of three small branches, each of which has its own weights. Extensiveexperiments demonstrate that our network significantly outperforms overstate-of-the-art methods in both visual quality and quantitative metrics.

RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement