To address color distortion， low contrast， and blurred details in underwater images， a novel enhancement algorithm based on a multi-branch residual attention network is proposed. Initially， a multi-branch color enhancement module is integrated before and after the encoder and decoder to adaptively correct image color deviations. Subsequently， a residual attention module is incorporated at the network’s bottleneck to mitigate feature loss between the encoder and decoder， thereby improving image detail preservation. A composite feature loss function is employed to facilitate comprehensive feature learning and effective retention of edge information. Experimental results demonstrate that the proposed algorithm achieves superior performance in both subjective perception and objective evaluation metrics. Specifically， the average peak signal-to-noise ratio （PSNR） and structural similarity index （SSIM） on the LUSI test set reach 27.420 dB and 0.885， representing improvements of 3.9% and 0.8%， respectively， over the next best method. On the EVUP test set， PSNR and SSIM attain 26.159 dB and 0.851， with enhancements of 3.3% and 1.3%， respectively. These results confirm the algorithm's effectiveness and robustness in underwater image quality enhancement， offering a valuable approach for image analysis in underwater engineering applications.