利用偏振图像加权融合及CLAHE算法的水下成像方法

针对传统基于偏振差分原理的水下光学成像方法中目标退偏振特性差异引起的图像中局部反射光损失的问题，本文提出了基于偏振图像加权融合与限制对比度自适应直方图均衡（Contrast Limited Adaptive Histogram Equalization, CLAHE）算法的水下成像方法。一方面，将原始水下图像分解为偏振光强图像和非偏振光强图像，根据不同退偏振特性目标在两幅图像中的灰度值分布特点，设计相应的权重因子，对两幅图像进行加权融合。从而实现在压缩原始水下图像中散射光的同时，保留更多目标反射光，提升整体目标反射光在融合图像中所占的比例。另一方面，为了进一步提升融合图像的对比度，利用限制对比度自适应直方图均衡算法对融合图像进行处理。该算法能够在提升融合图像对比度的同时，有效避免图像噪声的放大。实验结果表明，相比于传统的偏振差分方法以及独立的直方图均衡化算法，本文提出的算法能够有效提升水下图像的清晰度和对比度。

Method of Polarization Image Weighted Fusion and CLAHE for Underwater Imaging

In traditional underwater optical imaging methods based on the polarization difference principle, the local reflected light will be lost in the image caused by the difference in target depolarization characteristics. This paper proposes an underwater imaging method that combines the weighted fusion of polarized images and contrast limited adaptive histogram equalization algorithm. On the one hand, original underwater images can be decomposed into polarized light intensity and non-polarized light intensity images. The two images are weighted and fused by designing corresponding weighting factors according to the characteristics of the gray value distribution of the targets with different depolarization characteristics in the two images. This way, while compressing the scattered light in the original underwater image, more target reflected light is retained, and the proportion of the overall target reflected light in the fused image increases. On the other hand, to further improve the contrast of the fused image, the fused image is processed using the restricted contrast adaptive histogram equalization algorithm. This algorithm can effectively avoid the amplification of image noise while improving the contrast of the fused image. The experimental results show that the algorithm proposed in this paper can effectively improve the sharpness and contrast of underwater images compared with the traditional polarization difference method and the independent histogram equalization algorithm.