目的 为解决户外视觉系统在恶劣环境下捕捉图像存在细节模糊、对比度较低等问题，提出一种基于变差函数和形态学滤波的图像去雾算法（简称IDA_VAM）。方法 该算法首先利用变差函数获取较准确的全局环境光值，然后对最小通道图采用多结构元形态学开闭滤波器获取粗略的大气散射图，进而估计大气透射率并进行修正，接着采用双边滤波对其进行平滑操作，最后通过物理模型得到复原图像并进行色调调整，获取明亮、清晰无雾的图像。结果 本文算法与多种图像去雾算法进行对比，在含有雾气的近景图像、远景图像以及有明亮区域的图像均能很好地去除雾气，图像的信息熵值相对提高了38.0%，对比度值相对提高了34.1%，清晰度值相对提高了134.5%，得到较好的复原效果，获取一幅自然明亮的无雾图像。结论 大量仿真实验结果证实，IDA_VAM能够很好地恢复非复杂场景下的近景图像、远景图像以及含有明亮区域图像的色彩和清晰度，获得清晰明亮的无雾图像，细节可见度较高，且算法的时间复杂度与图像像素点个数呈线性关系，具有较好的实时性。

Objective To solve blurring and low contrast of images captured by outdoor visual systems under bad weather conditions, a new fast image defogging algorithm called IDA_VAM is presented; this algorithm is based on variogram and multiple structure element morphological open-and-close filter. Method The algorithm initially uses a variogram to obtain an accurate atmospheric optical value, and then exploits a multiple structure element morphological open-and-close filter toward the minimum channel map to obtain a rough scattering map. The transmittance map is estimated and corrected, and a bilateral filter is used for smooth operation. Recovery images are obtained by the physical model, and color adjustments are made to obtain bright, clear, and non-foggy images. Result Compared with other image defogging algorithms, the proposed method utilized for foggy images containing close range image, image perspective, and image with bright areas can be effective in removing the fog. The information entropy is relatively increased by 38.0%, and the contrast value is relatively increased by 34.1%. The definition value is relatively increased by 134.5%. Moreover, better restoration effect is obtained, thereby achieving a more natural, bright, and haze-free image. Conclusion A large number of experimental results show that this algorithm can effectively recover color and definition of the foggy image containing a close-range image, image perspective, and image with bright areas under non-complex scenes. Clear and natural fog-free images with details of higher visibility can be obtained, and time complexity of the IDA_VAM algorithm and number of image pixels are linearly correlated, thereby meeting real-time requirements.