【摘要】 由于矿井下光线不足,照度低且粉尘大,造成监控视频图像存在昏暗和模糊问题,利用小波变换获取视频画面中的不同频率分量信息,首先对低频分量采用暗原色先验进行去雾处理,然后用阈值滤波对高频分量进行消噪,将处理以后的低频分量和高频分量进行融合,重构小波函数,实现图像的增强。仿真实验结果表明所提算法能提高图像对比度,增强图像细节信息,淡化浓雾、抑制噪声等方面有较好的效果。在矿井运动目标检测中,为了改善传统混合高斯模型像素点不能精确匹配及参数迭代速度慢的问题,采用三帧差分法融合混合高斯背景模型,融合后的算法有效消除了背景更新不及时而导致的画面鬼影现象,而且运算速度得到明显提升,实现了运动目标实时追踪的需求。仿真实验结果表明所提算法相对传统混合高斯模型算法不仅能够快速的检测出运动目标,而且检测图像边缘细节信息更加清晰,并且能够解决物体遮挡等问题,为矿井视频信息处理和人员安全监测奠定了良好的基础。更多还原

【Abstract】 Aiming at the problems of dim lighting,low illumination and large dust in underground mine,which would lead to the dim and fuzzy of video,wavelet transformation is used to get the component of different frequency. The low-frequency component is firstly defogged using dark primary priori,and highfrequency component is denoised by threshold filter. The processed low-frequency component and highfrequency component are fused to reconstruct the wavelet function to realize the image enhancement. The simulation results show that the proposed algorithm can improve image contrast,enhance image details,reduce fog and suppress noise. Aiming at the problem that the traditional Gaussian mixture model can neither accurately match the pixels nor update the parameters fast in moving target detection,the threeframe difference method is used to fuse the Gaussian mixture background model. The fused algorithm eliminates the ghost phenomenon of the Gaussian background model caused by the untimely updating of the background,and improves the operation speed of the algorithm,meeting the real-time requirement.The results show that the proposed algorithm can not only detect moving targets faster than the traditional Mixture Gauss Model algorithm,but also detect image edge details more clearly,and can solve the problem of object occlusion,which lays a good foundation for mine video information processing and personnel safety monitoring.更多还原