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

Abstract

Single-pixel imaging requires only a unit detector with no spatial resolution capability to acquire spatial information of the target and reconstruct the image. However, the quality of reconstructing images strongly depends on measurement matrices and their number of samples, making it challenging to achieve high-quality imaging with fewer samples. In this Letter, a dataset-driven low-sampling-rate single-pixel imaging method is proposed. It utilizes a network model driven by the image datasets to directly extract target feature information from a small number of samples and reconstruct the image. Experimental results demonstrate that, compared to traditional single-pixel imaging methods, this method no longer depends strongly on the relationship between the measurement matrices and the samples, and it can achieve an ideal imaging effect with a structural similarity of 90.20% at low sampling rates.