3D Single-pixel imaging with active sampling patterns and learning based reconstruction

Single-pixel imaging (SPI) is significant for applications constrained by transmission bandwidth or lighting band, where 3D SPI can be further realized through capturing signals carrying depth. The sampling strategy and the reconstruction algorithm are the key issues of SPI. Traditionally, random patterns are often adopted for sampling, but this blindly passive strategy requires a high sampling rate, and even so, it is difficult to develop a reconstruc-tion algorithm that can maintain high accuracy and robustness. In this paper, an active and flexible sampling strategy is proposed to make up for the shortcoming of the single-pixel detector (SPD) that lacks spatial resolu-tion capability. The sampling rate and sampling order can be controlled actively and flexibly by designing the sampling patterns according to the needs. Furthermore, two approaches of deep learning are proposed to improve the quality of reconstruction, where a large amount of training data for 3D SPI are generated by a virtual system with graphic software. The ability of deep learning to reconstruct desired information under low sampling rates is analyzed. Abundant experiments verify that our method improves the precision of SPI even if the sampling rate is very low, which has the potential to be extended in similar systems or applied in practice.