Compressive sensing tomographic reconstruction of non-amplifying in-line hologram based on variable density downsampling in frequency domain

Wu Xiaoyan; Yu Yingjie; Bai Yuewei; Nie Li; Liu Kai; Pan Fangyu; Wang Xiaogang

doi:10.3788/IRLA20190500

Volume 49 Issue S1

Sep. 2020

Turn off MathJax

Article Contents

Article Navigation > Infrared and Laser Engineering > 2020 > 49(S1): 20190500

Wu Xiaoyan, Yu Yingjie, Bai Yuewei, Nie Li, Liu Kai, Pan Fangyu, Wang Xiaogang. Compressive sensing tomographic reconstruction of non-amplifying in-line hologram based on variable density downsampling in frequency domain[J]. Infrared and Laser Engineering, 2020, 49(S1): 20190500. doi: 10.3788/IRLA20190500

Citation:

Wu Xiaoyan, Yu Yingjie, Bai Yuewei, Nie Li, Liu Kai, Pan Fangyu, Wang Xiaogang. Compressive sensing tomographic reconstruction of non-amplifying in-line hologram based on variable density downsampling in frequency domain[J]. Infrared and Laser Engineering, 2020, 49(S1): 20190500. doi: 10.3788/IRLA20190500

Compressive sensing tomographic reconstruction of non-amplifying in-line hologram based on variable density downsampling in frequency domain

doi: 10.3788/IRLA20190500

1. School of Intelligent Manufacturing & Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China;
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

Received Date: 2019-12-11
Rev Recd Date: 2020-01-21
Publish Date: 2020-09-22

Abstract

A frequency-domain variable density downsampling method was applied to the reconstruction of compressive sensing tomography for non-amplifying in-line hologram. The purpose was to extract a small amount of information from the frequency-domain of non-amplifying in-line hologram and realize the reconstruction of compressed sensing tomography from a small amount of data in the frequency-domain of the hologram. Here, firstly it introduced the principle of combining three variable density downsampling with compressive sensing tomography reconstruction of hologram. Three kinds of variable density downsampling respectively were radial distribution, spiral distribution and exponential distribution variable density downsampling. Secondly, it carried out simulation and test experiments and analyzed the reconstruction quality of the methods for variable density downsampling combined with compressive holography. By experiments, it could be seen that:(1) three kinds of variable density downsampling could realize the extraction of a small amount of data for hologram in the frequency domain; (2) with the increase of sampling rate, the compression sensing tomography reconstruction quality of a small amount of data obtained by variable density reduction sampling was continuously improved; (3) under the sampling rate of less than 50%, exponential distribution downsampling had higher reconstruction quality than the other two methods (for example, in the case of low downsampling rate of 15%, the reconstruction quality of exponential distribution was more obvious than the other two methods); (4) under the sampling rate of more than 50%, the tomographic reconstruction quality of the three downsampling modes was relatively high and basically consistent.
- in-line hologram,
- compressive sensing,
- inverse problems,
- tomographic reconstruction

References

[1]	Bharathi S S, Radhakrishnan N, Prasad P B. Machine vision solutions in automotive industry[C]//Soft Computing Techniques in Engineering Applications. Berlin:Springer International Publishing, 2014.
[2]	Goodman J W, Lawrence R. Digital image formation from electronically detected holograms[J]. Applied Physics Letters, 1967, 11(3):77-79.
[3]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[4]	Rivenson Y, Stern A, Javidi B. Overview of compressive sensing techniques applied in holography[J]. Applied optics, 2003, 52(1):A423-A432.
[5]	Rivenson Y, Stern A, Javidi B. Improved depth resolution by single-exposure in-line compressive holography[J]. Applied Optics, 2013, 52(1):A223-A231.
[6]	David J B, Choi K, Daniel L M, et al. Compressive holography[J]. Optics Express, 2009, 17(15):13040-13049.
[7]	Brodoline A, Rawat N, Alexandre D, et al. 4D compressive sensing holographic imaging of small moving objects with multiple illuminations[J]. Applied Optics, 2019, 58(34):G127-G134.
[8]	Weng Jiawen, Li Hai, Yang Chuping, et al. Reconstruction of off-axis fresnel hologram by compressive sensing[J]. Opto-Electronic Engineering, 2015, 42(1):32-38.
[9]	de Souza J C, Freire Jr R B R, dos Santos P A M. Compressive holography with resolution improvement and lensless adjustable magnification[J]. Optics Communications, 2019, 437:337-341.
[10]	Wan Y, Man T, Wu F, et al. Parallel phase-shifting self-interference digital holography with faithful reconstruction using compressive sensing[J]. Optics and Lasers in Engineering, 2016, 86(1):38-43.
[11]	Leportier T, Park M C. Holographic reconstruction by compressive sensing[J]. Journal of Optics, 2017, 19(6):065704.
[12]	Hu Youjun, Zhou Xin, Yue Jianming, et al. Single pixel fourier hologram imaging system based on compressive sensing[J]. Laser Journal, 2016, 37(7):7-10. (in Chinese)
[13]	Weng Jiawen, Qin Yi, Yang Chuping, et al. Reconstruction of single low-coherence digital hologram by compressive sensing[J]. Laser & Optoelectronics Progress, 2015, 52:100901. (in Chinese)
[14]	Weng Jiawen, Yang Chuping, Li Hai. Self-interference incoherent digital holography by compressive sensing[J]. Acta Optica Sinica, 2016, 36(2):0209001. (in Chinese)
[15]	Sun A, Ding Z, Sheng Y, et al. Optical scanning holography based on compressive sensing using a digital micro-mirror device[J]. Optics Communications, 2017, 385:19-24.
[16]	Li Jun, Pan Yangyang, Li Jiaosheng, et al. Compressive holographic imaging based on single in-line hologram and superconducting nanowire single-photon detector[J]. Optics Communications, 2015, 355:326-330.
[17]	Li S, Zhao G, Sun H, et al. Compressive sensing imaging of 3-D object by a holographic algorithm[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(12):7295-7304.
[18]	Cull C F, Wikner D A, Mait J N, et al. Millimeter-wave compressive holography[J]. Applied Optics, 2010, 49(19):E67-E82.
[19]	Bioucas-Dias J M, Figueiredo M A T. A new twist:two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12):2992-3004.
[20]	Wang Z, Bovik A C. A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3):81-84.

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article Metrics

Article views(415) PDF downloads(11) Cited by()

Proportional views

Compressive sensing tomographic reconstruction of non-amplifying in-line hologram based on variable density downsampling in frequency domain

1. School of Intelligent Manufacturing & Control Engineering, Shanghai Polytechnic University, Shanghai 201209, China;

2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

Received Date: 2019-12-11

Rev Recd Date: 2020-01-21

Publish Date: 2020-09-22

Key words:

Abstract: A frequency-domain variable density downsampling method was applied to the reconstruction of compressive sensing tomography for non-amplifying in-line hologram. The purpose was to extract a small amount of information from the frequency-domain of non-amplifying in-line hologram and realize the reconstruction of compressed sensing tomography from a small amount of data in the frequency-domain of the hologram. Here, firstly it introduced the principle of combining three variable density downsampling with compressive sensing tomography reconstruction of hologram. Three kinds of variable density downsampling respectively were radial distribution, spiral distribution and exponential distribution variable density downsampling. Secondly, it carried out simulation and test experiments and analyzed the reconstruction quality of the methods for variable density downsampling combined with compressive holography. By experiments, it could be seen that:(1) three kinds of variable density downsampling could realize the extraction of a small amount of data for hologram in the frequency domain; (2) with the increase of sampling rate, the compression sensing tomography reconstruction quality of a small amount of data obtained by variable density reduction sampling was continuously improved; (3) under the sampling rate of less than 50%, exponential distribution downsampling had higher reconstruction quality than the other two methods (for example, in the case of low downsampling rate of 15%, the reconstruction quality of exponential distribution was more obvious than the other two methods); (4) under the sampling rate of more than 50%, the tomographic reconstruction quality of the three downsampling modes was relatively high and basically consistent.

HTML

Reference (20)

[1]	Bharathi S S, Radhakrishnan N, Prasad P B. Machine vision solutions in automotive industry[C]//Soft Computing Techniques in Engineering Applications. Berlin:Springer International Publishing, 2014.
[2]	Goodman J W, Lawrence R. Digital image formation from electronically detected holograms[J]. Applied Physics Letters, 1967, 11(3):77-79.
[3]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[4]	Rivenson Y, Stern A, Javidi B. Overview of compressive sensing techniques applied in holography[J]. Applied optics, 2003, 52(1):A423-A432.
[5]	Rivenson Y, Stern A, Javidi B. Improved depth resolution by single-exposure in-line compressive holography[J]. Applied Optics, 2013, 52(1):A223-A231.
[6]	David J B, Choi K, Daniel L M, et al. Compressive holography[J]. Optics Express, 2009, 17(15):13040-13049.
[7]	Brodoline A, Rawat N, Alexandre D, et al. 4D compressive sensing holographic imaging of small moving objects with multiple illuminations[J]. Applied Optics, 2019, 58(34):G127-G134.
[8]	Weng Jiawen, Li Hai, Yang Chuping, et al. Reconstruction of off-axis fresnel hologram by compressive sensing[J]. Opto-Electronic Engineering, 2015, 42(1):32-38.
[9]	de Souza J C, Freire Jr R B R, dos Santos P A M. Compressive holography with resolution improvement and lensless adjustable magnification[J]. Optics Communications, 2019, 437:337-341.
[10]	Wan Y, Man T, Wu F, et al. Parallel phase-shifting self-interference digital holography with faithful reconstruction using compressive sensing[J]. Optics and Lasers in Engineering, 2016, 86(1):38-43.
[11]	Leportier T, Park M C. Holographic reconstruction by compressive sensing[J]. Journal of Optics, 2017, 19(6):065704.
[12]	Hu Youjun, Zhou Xin, Yue Jianming, et al. Single pixel fourier hologram imaging system based on compressive sensing[J]. Laser Journal, 2016, 37(7):7-10. (in Chinese)
[13]	Weng Jiawen, Qin Yi, Yang Chuping, et al. Reconstruction of single low-coherence digital hologram by compressive sensing[J]. Laser & Optoelectronics Progress, 2015, 52:100901. (in Chinese)
[14]	Weng Jiawen, Yang Chuping, Li Hai. Self-interference incoherent digital holography by compressive sensing[J]. Acta Optica Sinica, 2016, 36(2):0209001. (in Chinese)
[15]	Sun A, Ding Z, Sheng Y, et al. Optical scanning holography based on compressive sensing using a digital micro-mirror device[J]. Optics Communications, 2017, 385:19-24.
[16]	Li Jun, Pan Yangyang, Li Jiaosheng, et al. Compressive holographic imaging based on single in-line hologram and superconducting nanowire single-photon detector[J]. Optics Communications, 2015, 355:326-330.
[17]	Li S, Zhao G, Sun H, et al. Compressive sensing imaging of 3-D object by a holographic algorithm[J]. IEEE Transactions on Antennas and Propagation, 2018, 66(12):7295-7304.
[18]	Cull C F, Wikner D A, Mait J N, et al. Millimeter-wave compressive holography[J]. Applied Optics, 2010, 49(19):E67-E82.
[19]	Bioucas-Dias J M, Figueiredo M A T. A new twist:two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12):2992-3004.
[20]	Wang Z, Bovik A C. A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3):81-84.

Compressive sensing tomographic reconstruction of non-amplifying in-line hologram based on variable density downsampling in frequency domain

doi: 10.3788/IRLA20190500

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views