雾天偏振成像影响分析及复原方法研究

Analysis and restoration research of fog polarization imaging

为了抑制雾天图像质量的退化,基于大气散射物理模型及偏振图像暗通道原理,提出了一种改进的雾天偏振遥感图像去雾算法。首先依据大气散射模型对雾天偏振成像机理进行分析,对大气偏振信息对去雾的影响进行了阐述。其次利用边缘检测和闭运算自动获取雾天偏振图像的天空区域,估算无穷远大气光强和大气偏振度。最后,针对图像中存在的噪声干扰等因素,修正大气偏振度及大气光强,恢复了退化图像的辐射强度信息。通过理论分析和实验验证,取得了较好的雾天图像复原结果。结果表明,该算法可以准确获取天空区域,实现更高鲁棒性的天空区域估计方法,有效提高图像的对比度和清晰度,增加图像细节,改善雾天图像的质量。该算法能够有效抑制雾天对图像造成的退化,从而提高遥感的目标探测和识别能力。

To solve the quality degradation problem of fog polarization imaging, an improved defogging method was proposed based on physical model of atmospheric scattering and dark passage principle of polarization images. Firstly, based on atmospheric scattering model, fog polarization imaging mechanism was analyzed and effect of atmospheric polarization on defogging was explained. Secondly, based on edge detection operator and closing operation, sky region of fog polarization image was obtained, and light intensity of infinity atmospheric and degree of polarization of atmospheric were estimated. At last, to solve the existing factors in the image such as noise interference, radiation intensity information of the degraded image was restored by modifying the degree of polarization and light intensity distribution of atmospheric. After theoretical analysis and experimental verification, good results of foggy image restoration were achieved. The results show that the algorithm can accurately obtain the sky region and improve the contrast and sharpness of the image, and improve the degradation of the image. Therefore, the algorithm can effectively inhibit the degradation of the image caused by fog, and improved target detection and identification capability of remote sensing.