中文摘要：

提出一种高频增强与神经网络相结合的图像亮度和对比度自适应增强方法。利用均值滤波获取原始图像的低频分量,由原始图像与低频分量的差值获取图像的高频分量。同时引入神经网络方法,建立图像的灰度均值、标准偏差与亮度和对比度两个增强系数的非线性映射关系,根据图像本身的均值与标准偏差自动获取增强系数,从而实现图像的亮度和对比度的自适应增强。该方法计算量小,实时性强,对亮度和对比度都较低的图像增强效果较好,可用于图像动态检测系统。为了验证算法的可行性,将所提出的方法应用到货车故障动态图像检测系统（TFDS）所采集的动态图像处理中,获得了好的效果。

英文摘要：

A novel adaptive image brightness and contrast enhancement method combining high-frequency emphasis with neural networks is proposed.First,the low frequency components of the image are obtained by average filter,then the high frequency components of the image can be obtained by subtracting the low frequency components from the original image,and the nonlinear mapping relation between the enhanced factors of image brightness and contrast,the mean and standard deviation of the original image is established based on neural network.The weighting factors are automatically determined by the constructed neural network in terms of the mean and standard deviation of the image.The new algorithm has very small computational compexity while still produces high contrast output images especially for low-intensity and low-contrast images,which makes it ideal to be implemented for on-line detection system based on dynamic image process.The proposed technique is tested in the images collected by trouble of moving freight car detection system（TFDS）,and a very good result has been obtained.