Fast high-resolution imaging combining deep learning and single-pixel imaging

Xuan Liu; Zilong Li; Jiaqing Dong; Guijun Wang; Wenhua Zhong; Qiegen Liu; Xianlin Song

doi:10.1117/12.2684974

31 May 2023 Fast high-resolution imaging combining deep learning and single-pixel imaging

Xuan Liu, Zilong Li, Jiaqing Dong, Guijun Wang, Wenhua Zhong, Qiegen Liu, Xianlin Song

Author Affiliations +

Proceedings Volume 12711, Third Optics Frontier Conference (OFS 2023); 127110W (2023) https://doi.org/10.1117/12.2684974
Event: Third Optics Frontier Conference (OFS 2023), 2023, Fuyang, China

Abstract

In the traditional Fourier single-pixel imaging (FSPI), compressed sampling is often used to improve the acquisition speed. However, the reconstructed image after compressed sampling often has a lower resolution and the quality is difficult to meet the imaging requirements of practical applications. To address this issue, we proposed a novel imaging method that combines deep learning and single-pixel imaging, which can reconstruct high-resolution images with only a small-scale sampling. In the training phase of the network, we attempted to incorporate the physical process of FSPI into the training process. To achieve this objective, a large number of natural images were selected to simulate Fourier single-pixel compressed sampling and reconstruction. The compressed reconstructed samples were subsequently employed for network training. In the testing phase of the network, the compressed reconstruction samples of the test dataset were input into the network for optimization. The experimental results showed that compared with traditional compressed reconstruction methods, this method effectively improved the quality of reconstructed images.

Citation Download Citation

Xuan Liu, Zilong Li, Jiaqing Dong, Guijun Wang, Wenhua Zhong, Qiegen Liu, and Xianlin Song "Fast high-resolution imaging combining deep learning and single-pixel imaging", Proc. SPIE 12711, Third Optics Frontier Conference (OFS 2023), 127110W (31 May 2023); https://doi.org/10.1117/12.2684974

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KEYWORDS

Image restoration

Deep learning

Quantum networks

Image quality

Education and training

Network architectures

Quantum deep learning

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