基于学习的光栅图像噪声抑制方法

基于条纹投影的三维形貌测量广泛应用于工业制造、质量检测、生物医疗、航空航天等领域。然而在高速测量的场景下，由于光栅图像的采集过程曝光时间短，三维重建结果通常会受到较为严重的图像噪声干扰。近年来，深度学习技术在计算机视觉等领域得到了广泛应用，并且取得了巨大的成功。受此启发，提出了一种基于学习的光栅图像噪声抑制方法。首先构建了一个基于U-net的卷积神经网络。其次在训练过程中，构建的神经网络学习从含有噪声的条纹图像到对应高质量包裹相位之间的映射关系。当经过适当训练，该网络可从含有噪声的条纹图像中准确恢复相位信息。实验结果表明:针对离线的快速运动场景三维测量，该方法仅利用一幅光栅图像可恢复高精度的相位信息，且相位精度优于传统的三步相移方法。该方法可为提升运动高速场景三维测量的精度提供切实可靠的解决方案。

A learning based on approach for noise reduction with raster images

Three-dimensional (3D) shape measurement based on fringe projection was widely used in industrial manufacturing, quality testing, biomedicine, aerospace and other fields. However, due to the short exposure time of raster images acquisition process, 3D reconstruction results were usually affected by serious image noise in the scene of high-speed measurement. In recent years, deep learning has been widely used in computer vision and other fields, and has achieved great success. Inspired by this, we proposed a learning based approach for noise reduction with raster images. Firstly, we constructed a convolutional neural network based on U-NET. Secondly, the neural network was constructed to learn the mapping relationship between the noisy fringe images and the corresponding high quality wrapped phase during the training process. With proper training, this network can accurately recovered phase information from noisy fringe images. Aiming at off-line 3D measurement in fast moving scene, experimental results show that the proposed method can recover high-precision phase information by using only one raster image, and the phase accuracy is better than the traditional three-step phase shift method. This method can provide a practical and reliable solution for improving the accuracy of 3D measurement in high-speed scene.