基于改进U-net的条纹图去噪分析

Fringe pattern denoising analysis based on improved U-net

基于变形条纹图分析的非接触三维光学测量中,从采集的变形条纹图中提取相位分布,进而获得被测形状的面信息,但是测量中获取的条纹图含有噪声,影响了提取相位信息的精度。为了更好更快的去除条纹图中的噪声,提出了一种改进U-net神经网络的深度学习滤波算法,在图像去噪领域,U-net获取的浅层特征较少,所提算法在U-net的卷积层中含有1×1的平行卷积分支,获取多尺度特征信息,分别添加1、2、3个1×1平行卷积分支进行实验。实验采用含有高密度区域的条纹图,并与目前最新的深度学习条纹图去噪算法对比,去噪效果提升0.9%,去噪效率提升41.7%,训练时间减少30.8%。

In non-contact 3 D optical measurement based on deformation fringe pattern analysis, phase distribution is extracted from the collected deformation fringe pattern to obtain the surface information of the measured shape. But the acquired fringe pattern contains noise in measurement, which affects the accuracy of extracting phase information. In order to remove the noise in fringe pattern better and faster, an improved U-net neural networks filtering algorithm based on deep learning is proposed. In the field of image denoising, U-net acquires few shallow features. The proposed method contains 1×1 parallel convolutional branches in the convolutional of U-net, which is used to obtain multi-scale feature. And adds 1,2, 3 1×1 parallel convolutional branches for experiment. Fringe pattern with high-density regions is used in the experiment, and the proposed method is compared with the state-of-the-art deep-learning fringe pattern denoising algorithm. The denoising effect of the proposed method is improved by 0.9%, the denoising efficiency is improved by 41.7% and the training time is reduced by 30.8%.