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

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 c...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 2560 - 2564
Main Authors Jiang, Jingxia, Bai, Jinbin, Liu, Yun, Yin, Junjie, Chen, Sixiang, Ye, Tian, Chen, Erkang
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Adaptation models
Adaptive Fourier Gating
Adaptive systems
Frequency-domain analysis
Image color analysis
Modulation
Multiscale Convolutional Attention
Scattering
Spatial and Frequency Domains
Underwater Image Enhancement
Visualization
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Summary: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.
DOI:10.1109/ICIP49359.2023.10222444