基于最大和最小光强图像的偏振去雾方法

相对于传统光学探测技术，偏振探测在目标探测、识别方面有着独特的优势。针对雾、霾等天气下图像退化的问题，提出一种利用偏振信息的图像去雾方法，通过获取3个角度下目标的偏振图像，求解出场景目标的斯托克斯矢量，从斯托克斯矢量与穆勒矩阵的关系出发，分析偏振图像光强随着偏振角度的变化规律，获取最大和最小光强下的正交偏振图像，利用偏振滤波和亮通道先验方法分别估算大气光偏振度和其无穷远处大气光强值，最终重构出无雾图像。实验结果表明，在雾霾天气下，利用获取的正交偏振图像能够重构出清晰的图像，且重构图像的平均梯度和边缘强度均提升了约3倍，灰度标准差提升了约88%。

Polarization defogging method based on maximum andminimum intensity images

Polarization imaging has unique advantages in target detection, recognition and processing compared with traditional photoelectric detection technology. To overcome degraged images taken in haze weather, an image defogging method based on polarization information is proposed. By obtaining target polarized image at three angles, Stokes vector of scene object is solved. From relationship between Stokes vector and Mueller matrix, variation law of polarized image intensity with polarization angle is analyzed, orthogonal polarization images under maximum and minimum light intensity are obtained automatically and accurately. Polarization degree of atmosphere and its infinity atmospheric light intensity value are estimated using polarization filtering and bright channel priori method, and fog-free images are reconstructed. Experimental results show that clear images can be reconstructed in haze weather by using orthogonal polarized images obtained, average gradient and edge intensity of reconstructed image are improved by about 3 times and grey standard deviation is improved by about 88%.