Work

Underwater Image Enhancement Using Dynamic Color Correction and Lightweight Attention-Embedded SRResNet

Year: 2025

Type: article

Abstract: An enhancement method integrating dynamic color correction with a lightweight residual network is proposed to resolve the challenges of color bias and insufficient contrast in underwater imaging. The dynamic color correction module is implemented based on the gray-world assumption, adaptively adjusting inter-channel color shifts to mitigate blue-green dominance in acquired images. Subsequently, the corrected images are processed through an improved SRResNet architecture incorporating lightweight residual blocks with embedded channel–spatial attention mechanisms, enhancing the responses of feature channels and the saliency of spatial regions Model complexity is reduced through depthwise separable convolutions and channel dimension reduction, ensuring computational efficiency. Validation on UIEB and RUIE datasets demonstrates superior qualitative and quantitative performance, achieving PSNR gains of 0.92–5.95 dB and UCIQE improvements of 0.14–0.74, compared with the established methodologies. Ablation studies quantify the contributions of the color correction and attention mechanisms to the overall enhancement efficiency, verifying the network’s effectiveness.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13081546

Authors: Kui Zhang, Yingying Zhang, Da Yuan, Xiandong Feng

Institutions: Qilu University of Technology, Institute of Oceanographic Instrumentation

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Shandong Academy of Sciences, Qilu University of Technology, National Key Research and Development Program of China