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

TL;DR

This paper introduces an active sampling strategy combined with deep learning for 3D single-pixel imaging, significantly reducing sampling rates while maintaining high reconstruction accuracy.

Contribution

It proposes a targeted active sampling pattern and a deep learning-based reconstruction method, enhancing 3D SPI performance under low sampling conditions.

Findings

Improved reconstruction accuracy at low sampling rates.

Effective targeted sampling patterns for 3D SPI.

Deep learning enhances robustness and precision.

Abstract

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. Sampling strategy and 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 reconstruction algorithm that can maintain higher accuracy and robustness. In this paper, an active strategy is proposed to perform sampling with targeted scanning by designed patterns, from which the spatial information can be easily reordered well. Then, deep learning methods are introduced further to achieve 3D reconstruction, and the ability of deep learning to reconstruct desired information under low sampling rates are 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 flexibly in similar systems according to practical needs.