# Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
###### Abstract  
Images of underwater environments suffer from contrast degradation, reduced clarity, and information attenuation. The traditional method is the global estimate of polarization. However, targets in water often have complex polarization properties. For low polarization regions, since the polarization is similar to the polarization of background, it is difficult to distinguish between target and non-targeted regions when using traditional methods. Therefore, this paper proposes a joint evaluation and partition fusion method. First, we use histogram stretching methods for preprocessing two polarized orthogonal images, which increases the image contrast and enhances the image detail information. Then, the target is partitioned according to the values of each pixel point of the polarization image, and the low and high polarization target regions are extracted based on polarization values. To address the practical problem, the low polarization region is recovered using the polarization difference method, and the high polarization region is recovered using the joint estimation of multiple optimization metrics. Finally, the low polarization and the high polarization regions are fused. Subjectively, the experimental results as a whole have been fully restored, and the information has been retained completely. Our method can fully recover the low polarization region, effectively remove the scattering effect and increase an image's contrast. Objectively, the results of the experimental evaluation indexes, EME, Entropy, and Contrast, show that our method performs significantly better than the other methods, which confirms the feasibility of this paper's algorithm for application in specific underwater scenarios. contrast enhancement; polarization partition optimization; joint evaluation; degraded image restoration; image fusion +
Footnote †: journal: Medical Imaging  
0000-0001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-8001-80001-8001-8001-8001-Traditional intensity-camera-captured underwater images cannot obtain accurate and reliable underwater image information, and cannot meet the actual needs of scientific researchers and explorers. In contrast, underwater image restoration based on polarization information is widely used nowadays [8]. Polarization imaging is able to separate the target and backscattered light from the target to obtain multi-dimensional information. Polarization is one of the intrinsic properties of light, and the underwater image recovery method based on polarization information usually collects polarized images in the same scene, combines the polarization information, and separates the background light and the target light, so as to achieve the purpose of recovering underwater images. Combining the polarization information, [PERSON] proposed a physical model-based algorithm to invert the underwater image degradation process; it takes into account the causes of image degradation and estimates the parameters in the underwater imaging model, which are effective in improving the visibility of degraded images to some extent. However, this method assumes that the light is uniform, but has some limitations [9; 10]. Since then, more and more researchers have improved upon this model. The traditional methods are poor for the recovery of highly polarized objects, and [PERSON] proposed a method based on curve fitting to estimate the transmittance and background light [11]. With this method, highly polarized objects in water can be well recovered. However, due to the introduction of a large number of nonlinear calculations, although the results are optimized, the processing time increases substantially. [PERSON] proposed a transmittance correction method [12] that uses a simple polynomial fit to correct the transmittance of objects with high polarization. The processing time of this method was significantly reduced compared to that of the method of [PERSON] et al.