DualOadamNet: Dual-Branch Lightweight Network for Underwater Image Processing with Optical-Aware Detail Augmentation

The deep water environment is complex and variable, and underwater images are easily affected by light scattering, water absorption and other factors, resulting in blurred details and color distortion. The existing enhancement methods generally suffer from poor model generalization, parallel task conflicts, and imbalance between real-time performance and optimization effect. To address this challenge, this study proposes a dual-branch lightweight network named DualOadamNet for underwater image processing with optical-aware detail augmentation. The model is based on the branches of global and local feature extraction, and the Optical-Aware Detail Augmentation Module with the characteristics of human visual simulation repair is introduced to repair the image naturally. Combined with pixel rearrangement operation, the model achieves efficient feature scale extraction. The experimental results on UIEB, EUVP and LSUI datasets show that the proposed method achieves average full-color evaluation metrics of 24.63 for PSNR, 0.919 for SSIM, and 3.267 for UIQM. Additionally, the real-time enhancement speed of a 1080p resolution image is 85.299 FPS.