Optimal parameters for image reconstruction in ghost imaging via sparsity constraints

Huizu Lin; Shuai Sun; Liang JiangLongkun DuHongkang HuWeitao Liu

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Optimal parameters for image reconstruction in ghost imaging via sparsity constraints

机译：Optimal parameters for image reconstruction in ghost imaging via sparsity constraints

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摘要

Ghost imaging via sparsity constraints (GISC) is an advanced imaging technique. The reconstruction quality of GISC is affected by the sparse ratio of the object, the regularization parameter, and the iteration number. Influences of these parameters on the peak signal-to-noise ratio (PSNR) of the reconstructed image are discussed and evaluated. The optimal regularization parameter and iteration number at different sparse ratios are given. Then the reconstructed images of GISC using the optimal parameters at different sparse ratios are shown. The improvement of the reconstruction quality of GISC utilizing the optimal parameters is confirmed through comparison with normalized ghost imaging. Finally, the reconstruction quality of GISC with random noise is analyzed, and a method to obtain the sparse ratio of the object by analyzing the signal of the bucket detector is discussed.

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来源
《Optical Engineering》 |2020年第12期|123101-1-123101-9|共9页
作者
Huizu Lin; Shuai Sun; Liang JiangLongkun DuHongkang HuWeitao Liu;
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作者单位

National University of Defense Technology, College of Liberal Arts and Science, Department of Physics, Changsha, China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类计量学;
关键词
compressed sensing; ghost imaging via sparsity constraints; regularization parameter; number of iterations; peak signal-to-noise ratio;

Optimal parameters for image reconstruction in ghost imaging via sparsity constraints

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