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.

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.