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.