Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15 g/ml, 0.30 g/ml, and 0.50 g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10 cm, 20 cm, 30 cm, and 40 cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper's algorithm is improved by about 69.26% over the original S0 image.