A Multi-Scale Contextual Fusion Residual Network for Underwater Image Enhancement

Underwater image enhancement (UIE) is a key technology in the fields of underwater robot navigation, marine resources development, and ecological environment monitoring. Due to the absorption and scattering of different wavelengths of light in water, the quality of the original underwater images usually deteriorates. In recent years, UIE methods based on deep neural networks have made significant progress, but there still exist some problems, such as insufficient local detail recovery and difficulty in effectively capturing multi-scale contextual information. To solve the above problems, a Multi-Scale Contextual Fusion Residual Network (MCFR-Net) for underwater image enhancement is proposed in this paper. Firstly, we propose an Adaptive Feature Aggregation Enhancement (AFAE) module, which adaptively strengthens the key regions in the input images and improves the feature expression ability by fusing multi-scale convolutional features and a self-attention mechanism. Secondly, we design a Residual Dual Attention Module (RDAM), which captures and strengthens features in key regions through twice self-attention calculation and residual connection, while effectively retaining the original information. Thirdly, a Multi-Scale Feature Fusion Decoding (MFFD) module is designed to obtain rich contexts at multiple scales, improving the model’s understanding of details and global features. We conducted extensive experiments on four datasets, and the results show that MCFR-Net effectively improves the visual quality of underwater images and outperforms many existing methods in both full-reference and no-reference metrics. Compared with the existing methods, the proposed MCFR-Net can not only capture the local details and global contexts more comprehensively, but also show obvious advantages in visual quality and generalization performance. It provides a new technical route and benchmark for subsequent research in the field of underwater vision processing, which has important academic and application values.