Optimization of random illumination pattern in computational ghost imaging

Shilun Sun; Shuquan Ma; Shaobo Li; Xuchao Liu

doi:10.1117/12.3018537

30 April 2024 Optimization of random illumination pattern in computational ghost imaging

Shilun Sun, Shuquan Ma, Shaobo Li, Xuchao Liu

Proceedings Volume 13153, Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems; 1315310 (2024) https://doi.org/10.1117/12.3018537
Event: Sixth Conference on Frontiers in Optical Imaging Technology and Applications (FOI2023), 2023, Nanjing, JS, China

Abstract

We proposed a method of optimizing random illumination pattern, to solve the problem that data acquisition is excessive when random pattern is used in computational ghost imaging. By introducing a transverse and longitudinal cyclic shift with a random number of moving pixels on the original random pattern, the illumination of light field is more uniform, which can further accelerate the convergence of reconstructing image quality and sampling data quantity relationship curve meanwhile reduce the system sample rate. In this paper, the peak signal to noise ratio is taken as the evaluation metric, and the effectiveness of the optimization method is verified by three sets of simulation experiments using different objects. The results show that the scheme has a certain universality, and has a certain guiding role for the experimental research of computational ghost imaging and the subsequent engineering application.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shilun Sun, Shuquan Ma, Shaobo Li, and Xuchao Liu "Optimization of random illumination pattern in computational ghost imaging", Proc. SPIE 13153, Sixth Conference on Frontiers in Optical Imaging and Technology: Novel Technologies in Optical Systems, 1315310 (30 April 2024); https://doi.org/10.1117/12.3018537

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