GLCE-Net: global-local feature collaborative enhancement network for underwater images toward object detection

Abstract

Abstract. Due to complex underwater illumination and scattering effects, underwater images commonly suffer from color distortion, low contrast, and structural blur. These distortions not only reduce visual perceptual quality but also severely limit the performance of downstream object detection tasks. Although existing underwater image enhancement methods have made notable progress in improving human visual perception, their optimization objectives mainly focus on subjective visual quality while overlooking detection-friendly feature representations. This results in a distributional mismatch with detector features, ultimately degrading object detection performance. To address these issues, we propose a global-local feature collaborative underwater image enhancement network. The network adopts a three-branch collaborative architecture to model local structural details, cross-color channel dependencies, and global contextual information, enabling joint optimization of global consistency and local discriminability. It provides more stable and discriminative feature representations for downstream object detection. To enhance the perception of small-scale targets and fine-grained structures, we design a structural detail enhancement module (SDEM) that combines multibranch and dilated convolutions to capture edge and texture features at different scales. The detail recalibration attention module (DRAM) combines local feature refinement with channel-spatial attention to adaptively reweight multibranch features, enabling fine-grained fusion of heterogeneous enhanced features and reinforcing key structural cues in the enhanced images. The experimental results show that our method significantly improves detection accuracy on the DUO and URPC2020 datasets. It also achieves strong PSNR and SSIM performance on the UIEB dataset. These results demonstrate that the proposed method can balance the detection performance and the visual quality.