# Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
###### Abstract
Recently, [PERSON] et al. recovered images based on the fact that polarization information has low-order properties, breaking through the limitations of the assumption of backward-scattered light in traditional polarization imaging methods [13]. Traditional methods are globally optimal for the entire image, but not for each target within the image. This may result in some targets not receiving image enhancement or even receiving degradation. [PERSON] et al. extracted the connectivity domain of low polarization and high polarization targets by rotating the polarization angle, which realized the transition from the global optimal estimation to the local optimal estimation [14]. Recently [PERSON] et al. also performed multiple partitions based on the polarization value of a target and recovered the partitions with different polarization values separately, but the method has too many partitions, which rather affects the quality of the final fused image [15]. Meanwhile, [PERSON] [16] et al. proposed a polarization parameter partitioning optimization recovery method for degraded underwater images, which allowed for the recovery of multiple targets, optimized the extraction method of different targets, and solved the problem that some of the targets could not be adequately recovered in the overall image. However, the method has certain shortcomings in recovering low polarization regions. The traditional method is to estimate the polarization of the whole image. But targets in water often have complex polarization properties. For low polarization regions, since their polarization is similar to the polarization of backscattered light, it is difficult to distinguish between target and non-targeted regions when using traditional methods of recovery; the recovered image is darkened and not even distinguishable from the background. [PERSON] et al. proposed a non-uniform illumination active underwater polarization imaging method based on complex polarization characteristics [17]. The recovery effect of this method is remarkable, and the EME index of the recovered image is improved significantly. However, in the case of a large gap between the polarization characteristics of the target, the recovery effect of his method is not satisfactory, and both the low polarization region and the high polarization region are not fully recovered, while our method proposed in this paper can perfectly solve this problem. The aim of our article is to recover the overall region efficiently when the target polarization characteristics are complex. In summary, the main organization of this paper can be summarized as follows: In Section 2.1, we first describe the scientific model of the imaging system and introduce the overall flow of the algorithm, which lays the foundation for the following. In Section 2.2, we preprocess Imin and Imax. In Section 2.3, we analyze the high and low polarization regions, and in Section 2.4, we recover the low polarization regions using polarization differencing. In Section 2.5, we estimate the target polarization using a joint evaluation, and fuse the high and low polarization regions. The experimental scenario and environment of this paper are described in Section 2.6. The experimental results are presented in Section 3. Finally, the full paper is summarized in Section 4.