Due to light scattering in water, underwater images commonly suffer from quality degradation. To address this issue, this paper proposes an enhancement model for turbid underwater polarized images based on an enhanced LU2Net network, validated using a self-constructed dataset. Initially, the acquired color polarization images are converted to grayscale. Complete linear polarization information is obtained by fusing the three polarization components at 0°, 45° and 90°. The degraded underwater polarized images are subsequently enhanced using the proposed enhanced LU2Net network model. Finally, enhanced images possessing richer detail features are acquired. Experimental results demonstrate that the proposed method outperforms comparative underwater image enhancement techniques including FUnIE-GAN and MLLE, in terms of both subjective and objective evaluations, as well as in the outcomes of feature point detection and Canny edge detection. Crucially, during feature point detection employing four distinct methods including ORB and AKAZE, the proposed approach consistently extracted a greater number of feature points.The proposed method achieves a 3.35% reduction in LPIPS compared to the best-performing existing method used for comparison. Furthermore, it increases the UCIQE score by 1.16% and decreases the NIQE score by 7.59% compared to the algorithm prior to enhancement. The proposed method successfully extracts clearer image edges, textures, and other fine details in turbid water environments under natural lighting conditions, thereby enhancing imaging quality in such challenging scenarios.