Robust underwater image enhancement with cascaded multi-level sub-networks and triple attention mechanism.

With the growing exploration of marine resources, underwater image enhancement has gained significant attention. Recent advances in convolutional neural networks (CNN) have greatly impacted underwater image enhancement techniques. However, conventional CNN-based methods typically employ a single network structure, which may compromise robustness in challenging conditions. Additionally, commonly used UNet networks generally force fusion from low to high resolution for each layer, leading to inaccurate contextual information encoding. To address these issues, we propose a novel network called Cascaded Network with Multi-level Sub-networks (CNMS), which encompasses the following key components: (a) a cascade mechanism based on local modules and global networks for extracting feature representations with richer semantics and enhanced spatial precision, (b) information exchange between different resolution streams, and (c) a triple attention module for extracting attention-based features. CNMS selectively cascades multiple sub-networks through triple attention modules to extract distinct features from underwater images, bolstering the network's robustness and improving generalization capabilities. Within the sub-network, we introduce a Multi-level Sub-network (MSN) that spans multiple resolution streams, combining contextual information from various scales while preserving the original underwater images' high-resolution spatial details. Comprehensive experiments on multiple underwater datasets demonstrate that CNMS outperforms state-of-the-art methods in image enhancement tasks.

Dual-path joint correction network for underwater image enhancement.

Acquired underwater images often suffer from severe quality degradation, such as color shift and detail loss due to suspended particles' light absorption and scattering. In this paper, we propose a Dual-path Joint Correction Network (DJC-NET) to cope with the above degenerate issues, preserving different unique properties of underwater images in a dual-branch way. The design of the light absorption correction branch is to improve the selective absorption of light in water and remove color distortion, while the light scattering correction branch aims to improve the blur caused by scattering. Concretely, in the light absorption correction path, we design the triplet color feature extraction module, which balances the triplet color distribution of the degraded image through independent feature learning between R, G, and B channels. In the light scattering correction path, we develop a dual dimensional attention mechanism to extract the texture information from the features, aiming to recover sufficient details by more effective feature extraction. Furthermore, our method utilizes the multi-scale U-net to adaptively fusion features from different paths to generate enhanced images. Extensive visual and objective experimental results demonstrate that our method outperforms state-of-the-art methods in various underwater scenes.