Abstract

Reducing the imaging time while maintaining reconstruction accuracy remains challenging for single-pixel imaging. One cost-effective approach is nonuniform sparse sampling. The existing methods lack intuitive and intrinsic analysis in sparsity. The lack impedes our comprehension of the form’s adjustable range and may potentially limit our ability to identify an optimal distribution form within a confined adjustable range, consequently impacting the method’s overall performance. In this Letter, we report a sparse sampling method with a wide adjustable range and define a sparsity metric to guide the selection of sampling forms. Through a comprehensive analysis and discussion, we select a sampling form that yields satisfying accuracy. These works will make up for the existing methods’ lack of sparsity analysis and help adjust methods to accommodate different situations and needs.

© 2023 Optica Publishing Group

Efficient large-scale single-pixel imaging

Daoyu Li, Zhijie Gao, and Liheng Bian
Opt. Lett. 47(21) 5461-5464 (2022)

Learning-based adaptive under-sampling for Fourier single-pixel imaging

Wenxin Huang, Fei Wang, Xiangyu Zhang, Ying Jin, and Guohai Situ
Opt. Lett. 48(11) 2985-2988 (2023)

Self-supervised learning for single-pixel imaging via dual-domain constraints

Xuyang Chang, Ze Wu, Daoyu Li, Xinrui Zhan, Rong Yan, and Liheng Bian
Opt. Lett. 48(7) 1566-1569 (2023)

Research on data-driven low-sampling-rate single-pixel imaging method

Shaosheng Dai, Ziqiang He, and Jinsong Liu
Opt. Lett. 48(23) 6132-6135 (2023)

Phase retrieval via nonlocal complex-domain sparsity

Liheng Bian, Xin Wang, Xuyang Chang, Zhijie Gao, and Tong Qin
Opt. Lett. 48(7) 1854-1857 (2023)