车载手势识别中基于小波变换和双边滤波的图像去噪方法

手势识别是人机交互中的重要研究领域，车载手势识别系统可以减少驾驶员手动操作仪表导致的分心，提高驾驶安全性。受光照变化、汽车环境、摄像头成像质量等各因素的影响，车载手势图像中常会存在大量复杂噪声。这些噪声严重影响后续手势分割、特征提取和手势识别的准确性。针对手势图像中存在的噪声问题，本文提出了一种适用于车载手势图像处理的新方法。该方法先对小波分解后的各高频子带采用不同方向的一维非线性扩散滤波处理得到初步去噪手势图，在此基础上用多尺度双边滤波对图像再次处理。实验结果表明，本文方法可以较好地去除车载手势图中噪声，抑制车载手势图细节的模糊。 

Image Denoising Method Based on Wavelet Transform and Bilateral Filter in Vehicle Gesture Recognition

Gesture interaction is the important research area in human-computer interaction. Vehicle gesture recognition system can reduce the distraction caused by operating instrument and improve the safety of driving. Influenced by illumination changes, the internal environment of the car, camera imaging quality and other factors, large amount of complex noise exists in the vehicle gesture images, which seriously affect the accuracy of gesture segmentation, feature extraction and gesture recognition. In this paper, an image processing method suitable for vehicle gesture images was proposed to solve this problem. In this method, one-dimensional nonlinear diffusion filtering was used to remove the noise in the high frequency sub band after wavelet decomposition and get the preliminary denoising image. Then, the preliminary denoising image was further denoised by multi-scale bilateral filtering. Experiment results show that the proposed method can better remove the noise and prevent the blurring of details of the vehicle gesture image than other methods.