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

• URL: http://arxiv.org/abs/2302.12186v1
• Date: Thu, 23 Feb 2023 17:27:05 GMT
• Title: RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
• Authors: Jingxia Jiang, Jinbin Bai, Yun Liu, Junjie Yin, Sixiang Chen, Tian Ye, Erkang Chen
• Abstract summary: We propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Conal Attention Module (MCAM) 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.
• Score: 5.3240763486073055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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