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.