CCMCS-Net: Integrating Color Correction and Multicolor-Space Stretching for Improving Underwater Image Quality

High-resolution underwater imagery is essential for advancing marine exploration, offering critical data to support resource analysis and discovery. However, underwater images frequently face issues like color loss and diminished contrast. To overcome these challenges, we propose CCMCS-Net, a network for enhancing underwater images that operate across multiple color spaces and are structured in two stages: color correction and contrast augmentation. A Color Correction Subnet (CC-Net) and a Multi Color-space Stretching Subnet (MCS-Net) are the two primary parts of the network. Initially, the Static Correction Module (SCM) leverages green channel data to adjust the red and blue channels, correcting color distortion in degraded images. At the same time, the Dynamic Correction Module (DCM) obtains weight mappings through end-to-end training. It combines SCM to achieve adaptive dynamic compensation of the image, which generalizes the deviation during correction to a certain extent. Next, the RGB, Lab, and HSI color distributions are individually adjusted by applying histogram stretching to the corrected image. The adjusted channels are then concatenated and fed into a Dynamic Fusion Module (DFM) for feature fusion across color spaces. This further enhances image visibility and contrast. The DCM and DFM are trained on our modified U-shaped network architecture. The network can adaptively select and emphasize the most valuable features for feature aggregation so that the aggregated features better promote high-quality image reconstruction. Extensive experiments demonstrate that CCMCS-Net delivers outstanding results in both visual assessments and objective metrics for real and synthetic underwater images.