Reconstruction method of computational ghost imaging based on non-local generalized total variation

JIANG Zonghua; TIAN Xin; YANG Jinling

doi:10.5768/JAO202243.0102001

Journal of Applied Optics > 2022 > 43(1): 52-59. > DOI: 10.5768/JAO202243.0102001

JIANG Zonghua, TIAN Xin, YANG Jinling. Reconstruction method of computational ghost imaging based on non-local generalized total variation[J]. Journal of Applied Optics, 2022, 43(1): 52-59. DOI: 10.5768/JAO202243.0102001

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Reconstruction method of computational ghost imaging based on non-local generalized total variation

School of Electronic Information, Wuhan University, Wuhan 430072, China

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Received Date: August 04, 2021
Revised Date: October 24, 2021
Available Online: December 15, 2021

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Abstract

Abstract

The ghost imaging is an imaging technology that can penetrate harsh environments such as the heavy fog. Aiming at the problems of more noise and lower image contrast of reconstructed images of traditional ghost imaging, the non-local generalized total variation method was applied for image reconstruction of ghost imaging, and the reconstruction method of computational ghost imaging based on non-local generalized total variation was proposed. The method constructed the non-local correlation weights to design the gradient operator, which was substituted into total variation reconstruction algorithm, so that the reconstructed images could effectively remove the noise while achieving the better detail restoration. The simulations were performed under different conditions, and the peak signal-to-noise ratio of proposed method was improved by about 1 dB compared with other methods, while it had better subjective visual effects. The experimental platform was designed and built to verify the effectiveness of the algorithm. The experimental results verify the superiority of the proposed method in terms of noise removal and detail reconstruction.
- computational ghost imaging,
- compressive sensing,
- non-local generalized total variation,
- spatial light modulator

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References

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Reconstruction method of computational ghost imaging based on non-local generalized total variation