目的 针对机器视觉系统在雾天条件下采集的图像存在对比度低、细节模糊的问题，提出一种结合图像融合与分割的场景复原方法。方法 基于光学反射成像的物理特性以及形态学运算分别获取雾气浓度的近似估计，计算图像的局部方差并利用加权融合的方法得出准确的大气耗散函数，通过分割雾气最浓区域或者天空区域求得精确的大气光值，最后由大气散射模型计算复原图像并进行亮度和色调的调整。结果 该方法可以有效避免光晕效应和天空颜色失真等不足，能快速复原场景的对比度和颜色。结论 实验结果表明，该方法的场景适应能力较强，复原效果和计算速度相比于前人的方法均有不同程度的提高。

Objective A scene restoration algorithm based on image fusion and segmentation is proposed to enhance the contrast and detail information of haze images captured by a machine vision system.Method Haze density is roughly estimated based on the physical properties of the optical reflectance imaging and morphology operation. The atmospheric veil is then estimated accurately by using weighted image fusion and by computing for the local variance. The global atmospheric light is obtained by segmenting the most hazed region or the sky part of the image. Finally, the ideal result is obtained through a physical model, and the brightness and chroma of the images are adjusted via tone mapping. Result This method can avoid halo artifacts or color distortion while achieving a good restoration of contrast and color fidelity. Conclusion Results show that the proposed method has robust scene adaptability and achieves different degrees of improvement in terms of restoration effect and computation speed.