An Underwater Image Restoration Method With Polarization Imaging Optimization Model for Poor Visible Conditions

Polarization imaging is extensively employed in underwater image restoration due to its effectiveness in removing backscattered light. However, existing polarization imaging methods generally assume the degree of polarization (DoP) of the backscattering is spatially constant and estimate it from the background region, limiting their practical applications. To address these challenges, we propose an underwater image restoration method based on a polarization imaging optimization model (PIOM). First, we develop a novel polarization image formation model by fusing the DoP and angle of polarization (AoP) of backscattered light. Second, we introduce an adaptive particle swarm local optimization (APSLO) method based on the PIOM. This method decomposes the image into small blocks and employs an objective optimization function to estimate the local optimal fusion parameters. Additionally, we propose a robust polynomial spatial fitting method to reduce block artifacts and noise disturbances, achieving globally optimal fusion parameters. Finally, we fully consider the advantages of gamma correction, and propose an adaptive contrast enhancement method to balance brightness and contrast. Experimental results show that our PIOM effectively removes backscattering while preserving finer details, colors, and contours. The code and datasets will be available at https://github.com/liyafengLYF/UIRPIOM.