Part-based image-loop network for single-pixel imaging

Li, Xuelong; Chen, Yifan; Tian, Tong; Sun, Zhe

doi:10.1016/j.optlastec.2023.109917

Optics & Laser Technology

2024

DOI: 10.1016/j.optlastec.2023.109917

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Part-based image-loop network for single-pixel imaging

Xuelong Li

Yifan Chen

Tong Tian

et al.

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2024

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Cited by 4 publications

References 30 publications

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Denoised single-pixel imaging in a Fourier acquisition mode

Xia,

Wang,

Zhao

2024

Appl. Opt.

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The degradation of imaging quality caused by environmental noise during Fourier single pixel imaging (FSPI) is a big problem. In this paper, we propose simple and efficient denoised single-pixel imaging schemes by using linear filters to reduce the impact. Two filters, such as an average filter and Gaussian filter, are employed, and their corresponding schemes are named SCH-A and SCH-G, respectively. The experimental and simulation results show that both schemes can effectively reduce the impact of environmental noise and have greater robustness in comparison with those using the deringing SPI and conventional SPI. Compared with SCH-A, the reconstructed image by SCH-G keeps more details and edges. With the increase of template size, SCH-A and SCH-G have better filtering effects with the same variance. Meanwhile, the larger the template size is, the better filtering effect SCH-A has. This conclusion is also applicable to SCH-G under the same variance.

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Denoised single-pixel imaging in a Fourier acquisition mode

Xia,

Wang,

Zhao

2024

Appl. Opt.

View full text Add to dashboard Cite

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Multi-input mutual supervision network for single-pixel computational imaging

Geng,

Sun,

Chen

et al. 2024

Opt. Express

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In this study, we propose a single-pixel computational imaging method based on a multi-input mutual supervision network (MIMSN). We input one-dimensional (1D) light intensity signals and two-dimensional (2D) random image signal into MIMSN, enabling the network to learn the correlation between the two signals and achieve information complementarity. The 2D signal provides spatial information to the reconstruction process, reducing the uncertainty of the reconstructed image. The mutual supervision of the reconstruction results for these two signals brings the reconstruction objective closer to the ground truth image. The 2D images generated by the MIMSN can be used as inputs for subsequent iterations, continuously merging prior information to ensure high-quality imaging at low sampling rates. The reconstruction network does not require pretraining, and 1D signals collected by a single-pixel detector serve as labels for the network, enabling high-quality image reconstruction in unfamiliar environments. Especially in scattering environments, it holds significant potential for applications.

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Multi-polarization fusion network for ghost imaging through dynamic scattering media

Lu,

Sun,

Chen

et al. 2024

Advanced Imaging

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Part-based image-loop network for single-pixel imaging

Cited by 4 publications

References 30 publications

Denoised single-pixel imaging in a Fourier acquisition mode

Denoised single-pixel imaging in a Fourier acquisition mode

Multi-input mutual supervision network for single-pixel computational imaging

Multi-polarization fusion network for ghost imaging through dynamic scattering media

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