全局参数估计的水下目标偏振复原方法

水体介质对光的吸收和散射是导致水下成像退化的两大主要影响因素。针对水下目标成像,本文提出了一种全局参数估计的水下目标偏振复原方法,利用构建的精简水下目标偏振重构模型,通过自动估计全局最优偏振信息重构参数,复原出水下目标图像,降低水体对图像质量的影响。在估计重构参数的过程中,首先,采用偏振中值滤波方法和基于亮原色原理的方法,分别估算水下背景光偏振度信息和无穷远处水下背景光强值;再利用基于最小互信息原则对估计的背景光偏振度信息进行优化;然后采用水下目标偏振图像增强算法将得到的水下目标重构图进行细节增强处理,最终获得复原后的水下目标辐射信息图。实验结果表明,在性能评价指标方面,相较于水下原图和其他水下复原方法处理图,利用本文方法处理后所得的图像增强测量值EME平均提高了120%,图像质量得到了明显的改善。该方法解决了人工取景估计参数不佳的问题,提高了复原目标图像的对比度,可以用于浑浊水下的目标探测与识别。

Underwater target polarization recovery method based on global parameter estimation

The absorption and scattering of underwater light by water are the two main factors influencing image degradation. With a focus on underwater target imaging, this paper proposes an underwater target polarization recovery method by global parameter estimation, using a simplified underwater target polarization reconstruction model, which recovers an underwater target image by automatically estimating global optimal polarization information reconstruction parameters to reduce the impact of the medium on image quality. First, a polarization median filter method and the bright primary principle method were used to estimate the underwater background light polarization degree information and the infinite underwater background light intensity values, respectively. Based on the principle of minimum mutual information, the background light polarization degree information was optimized and estimated. Then, adopting the underwater target polarization image enhancement algorithms to enhance the details of the underwater reconstruction target, a clear figure of the underwater target's radiation information was produced. The experimental results show that the value of the measure of enhancement (EME) in the suggested method increased by 120% on average, compared with the original strength of the figure and that of other underwater recovery methods for the calculation of the performance evaluation index. Further, the image quality was significantly improved. The method has solved the inaccuracy problem while estimating parameters via artificial view and improved the contrast of the restored target image. The method can be used to detect and identify turbid underwater targets.