Affiliation:
1. Zhejiang Sci-Tech University
2. Harbin Engineering University
3. Harbin Institute of Technology
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.
Funder
Open Foundation of Key Laboratory of Optical Field Manipulation of Zhejiang Province
Zhejiang Sci-Tech University
Natural Science Foundation of Zhejiang Province
National Natural Science Foundation of China
Subject
Atomic and Molecular Physics, and Optics
Cited by
10 articles.
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