Self super-resolution autostereoscopic 3D measuring system using deep convolutional neural networks

Autostereoscopy technology can provide a rapid and accurate three-dimensional (3D) measurement solution for micro-structured surfaces. Elemental images (EIs) are recorded within one snapshot and the measurement accuracy can be quantified from the disparities existing in the 3D information. However, a trade-off between the spatial and the angular resolution of the EIs is a major obstacle to the improvement on the measurement results. To address this issue, an angular super-resolution algorithm based on deep neural networks is proposed to construct a self super-resolution autostereoscopic (SSA) 3D measuring system. The proposed super-resolution algorithm can generate novel perspectives between the neighboring EIs so that the angular resolution is enhanced. The proposed SSA 3D measuring system can achieve self super-resolution on its measurement data. A comprehensive comparison experiment was conducted to verify the feasibility and technical merit of the proposed measuring system. The results show that the proposed SSA system can significantly improve the resolution of the measuring data by around 4 folds and enhance the measurement accuracy to a sub-micrometer level with lower standard deviations and biases.