Underwater polarization image restoration based on a partition method

Abstract

The quality of underwater polarization imaging is mainly affected by the polarization properties of the target and impurity particles. Traditional methods often assume uniform polarization characteristics of the target, which make it difficult to address the restoration issues of complex targets. In response, a partition-based method for recovering underwater polarization images is proposed. The method involves preprocessing the image using the Gaussian curvature filtering algorithm, partitioning the image based on polarization information. In addition, a joint image evaluation method is used to achieve restoration of complex polarized characteristic targets. The method estimates the value of the reflected light polarization of one partition to estimate the value of the next partition and links the polarization values of each partition. Our approach achieves clear restoration results for multiple targets or complex structural objects underwater. Achieving significant improvement in image quality in multi-target underwater scenes, our method is highly effective for complex underwater environments. Experimental results show that our method, when compared with three other newer methods on multiple images of different targets and under varying scattering conditions, achieves an average increase of 617% in the standard deviation image evaluation index and a 61% optimization in the natural image quality evaluator index. Furthermore, our method is robust for different degrees of water turbidity.

Breakthrough Underwater Physical Environment Limitations on Optical Information Representations: An Overview and Suggestions

“…The method then utilizes optical attenuation characteristics to calculate the color loss rate across different underwater scenes. The authors of [68] detailed a method for recovering underwater polarized images, employing Gaussian curvature filtering for preprocessing, partitioning images based on polarization information, and then utilizing a joint image evaluation method for complex polarized characteristic target recovery. The authors of [69] introduced an underwater image restoration method that estimates background light and calculates scene depth and transmission maps to restore the image by inverting the underwater image formation model.…”