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

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.