Fourier single pixel imaging reconstruction method based on the U-net and attention mechanism at a low sampling rate
Pengfei Jiang, Jianlong Liu, Long Wu, Lu Xu, Jiemin Hu, Jianlong Zhang, Yong Zhang, Xu Yang
Abstract
There exists the contradiction between imaging efficiency and imaging quality for Fourier single-pixel imaging (FSI). Although the deep learning approaches have solved this problem to some extent, the reconstruction quality at low sampling rate is still not enough to meet the practical requirements. To solve this problem, inspired by the idea of super-resolution, this paper proposes the paralleled fusing of the U-net and attention mechanism to improve the quality of FSI reconstruction at a low sampling rate. This paper builds a generative adversarial network structure to achieve recovery of high-resolution target images from low-resolution FSI reconstruction results under low sampling rate conditions. Compared with conventional FSI and other deep learning methods based on FSI, the proposed method can get better quality and higher resolution results at low sampling rates in simulation and experiments. This approach is particularly important to high-speed Fourier single pixel imaging applications.