Improving the performance of ghost imaging via measurement-driven framework

Abstract High-quality reconstruction under a low sampling rate is very important for ghost imaging. How to obtain perfect imaging results from the low sampling rate has become a research hotspot in ghost imaging. In this paper, inspired by matrix optimization in compressed sensing, an optimization s...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hanqiu Kang, Yijun Wang, Ling Zhang, Duan Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e483c2557bc94691a4983e78d3a97ea3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e483c2557bc94691a4983e78d3a97ea3
record_format	dspace
spelling	oai:doaj.org-article:e483c2557bc94691a4983e78d3a97ea32021-12-02T16:35:56ZImproving the performance of ghost imaging via measurement-driven framework10.1038/s41598-021-86275-22045-2322https://doaj.org/article/e483c2557bc94691a4983e78d3a97ea32021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86275-2https://doaj.org/toc/2045-2322Abstract High-quality reconstruction under a low sampling rate is very important for ghost imaging. How to obtain perfect imaging results from the low sampling rate has become a research hotspot in ghost imaging. In this paper, inspired by matrix optimization in compressed sensing, an optimization scheme of speckle patterns via measurement-driven framework is introduced to improve the reconstruction quality of ghost imaging. According to this framework, the sampling matrix and sparse basis are optimized alternately using the sparse coefficient matrix obtained from the low-dimension pseudo-measurement process and the corresponding solution is obtained analytically, respectively. The optimized sampling matrix is then dealt with non-negative constraint and binary quantization. Compared to the developed optimization schemes of speckle patterns, simulation results show that the proposed scheme can achieve better reconstruction quality with the low sampling rate in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity index (MSSIM). In particular, the lowest sampling rate we use to achieve a good performance is about 6.5%. At this sampling rate, the MSSIM and PSNR of the proposed scheme can reach 0.787 and 17.078 dB, respectively.Hanqiu KangYijun WangLing ZhangDuan HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hanqiu Kang Yijun Wang Ling Zhang Duan Huang Improving the performance of ghost imaging via measurement-driven framework
description	Abstract High-quality reconstruction under a low sampling rate is very important for ghost imaging. How to obtain perfect imaging results from the low sampling rate has become a research hotspot in ghost imaging. In this paper, inspired by matrix optimization in compressed sensing, an optimization scheme of speckle patterns via measurement-driven framework is introduced to improve the reconstruction quality of ghost imaging. According to this framework, the sampling matrix and sparse basis are optimized alternately using the sparse coefficient matrix obtained from the low-dimension pseudo-measurement process and the corresponding solution is obtained analytically, respectively. The optimized sampling matrix is then dealt with non-negative constraint and binary quantization. Compared to the developed optimization schemes of speckle patterns, simulation results show that the proposed scheme can achieve better reconstruction quality with the low sampling rate in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity index (MSSIM). In particular, the lowest sampling rate we use to achieve a good performance is about 6.5%. At this sampling rate, the MSSIM and PSNR of the proposed scheme can reach 0.787 and 17.078 dB, respectively.
format	article
author	Hanqiu Kang Yijun Wang Ling Zhang Duan Huang
author_facet	Hanqiu Kang Yijun Wang Ling Zhang Duan Huang
author_sort	Hanqiu Kang
title	Improving the performance of ghost imaging via measurement-driven framework
title_short	Improving the performance of ghost imaging via measurement-driven framework
title_full	Improving the performance of ghost imaging via measurement-driven framework
title_fullStr	Improving the performance of ghost imaging via measurement-driven framework
title_full_unstemmed	Improving the performance of ghost imaging via measurement-driven framework
title_sort	improving the performance of ghost imaging via measurement-driven framework
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e483c2557bc94691a4983e78d3a97ea3
work_keys_str_mv	AT hanqiukang improvingtheperformanceofghostimagingviameasurementdrivenframework AT yijunwang improvingtheperformanceofghostimagingviameasurementdrivenframework AT lingzhang improvingtheperformanceofghostimagingviameasurementdrivenframework AT duanhuang improvingtheperformanceofghostimagingviameasurementdrivenframework
_version_	1718383725047185408

Improving the performance of ghost imaging via measurement-driven framework

Ejemplares similares