Toward Real-World Super-Resolution Technique for Fringe Projection Profilometry
Pengcheng Yao, Shaoyan Gai, Feipeng Da
Abstract
Deep learning technique has exhibited promising performance in achieving high-resolution (HR) image from their low-resolution (LR) image in super-resolution (SR) field. However, most of the existing SR methods have two underlying problems. Firstly, degraded datasets (i.e., bicubic downsampling) are usually used to train and evaluate network model, which maybe lead to less effective in practical scenarios. Secondly, 2D-to-3D SR technique is lacking. In this paper, a real-world 2D-to-3D technique is developed to realize SR 3D shape from 2D fringe images in fringe projection profilometry (FPP). A FPP system consisting of one projector and a dual-camera is applied to obtain the real-world dataset where paired LR-HR images on same scene are captured. The 3D geometrical constraints solved from FPP system are employed to align the image pairs by pixel-to-pixel mapping so that more accurate dataset can be obtained. In addition, a flexible multiple-to-two network structure is introduced to achieve SR 3D point cloud from multiple phase-shifting patterns. Experiments demonstrate the comparing between traditional degraded training and our training.