Please wait a minute...
Chin. Phys. B, 2021, Vol. 30(1): 014208    DOI: 10.1088/1674-1056/abb3ea
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS Prev   Next  

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Fan Liu(刘璠)1,3, Xue-Feng Liu(刘雪峰)1,3,†, Ruo-Ming Lan(蓝若明)2,‡, Xu-Ri Yao(姚旭日)1,3, Shen-Cheng Dou(窦申成)1,3, Xiao-Qing Wang(王小庆)1, and Guang-Jie Zhai(翟光杰)1,3
1 Key Laboratory of Electronics and Information Technology for Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; 2 School of Physics and Electronics, Shandong Normal University, Ji'nan 250014, China; 3 University of Chinese Academy of Sciences, Beijing 100049, China
Abstract  Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing (CS) imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error. Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.
Keywords:  compressed sensing      imaging quality      spatial frequency domain      multi-scale modulation  
Received:  29 June 2020      Revised:  20 July 2020      Accepted manuscript online:  01 September 2020
PACS:  42.30.-d (Imaging and optical processing)  
  42.30.Va (Image forming and processing)  
  42.30.Wb (Image reconstruction; tomography)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61601442, 61605218, and 61575207), the National Key Research and Development Program of China (Grant No. 2018YFB0504302), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant Nos. 2015124 and 2019154).
Corresponding Authors:  Corresponding author. E-mail: liuxuefeng@nssc.ac.cn Corresponding author. E-mail: lanrm0616@163.com   

Cite this article: 

Fan Liu(刘璠), Xue-Feng Liu(刘雪峰), Ruo-Ming Lan(蓝若明), Xu-Ri Yao(姚旭日), Shen-Cheng Dou(窦申成), Xiao-Qing Wang(王小庆), and Guang-Jie Zhai(翟光杰) Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain 2021 Chin. Phys. B 30 014208

1 Donoho D L 2006 IEEE Trans. Inf. Theory 52 1289
2 Cand\`es E J and Tao T 2006 IEEE Trans. Inf. Theory 52 5406
3 Duarte M F, Davenport M A, Takhar D, Laska J N, Sun T, Kelly K F and Baraniuk R G 2008 IEEE Sign. Process. Mag. 25 83
4 Arce G R, Brady D J, Carin L, Arguello H and Kittle D S 2014 IEEE Sign. Process. Mag. 31 105
5 Pian Q, Yao R Y, Sinsuebphon N and Intes X 2017 Nat. Photon. 11 411
6 Zhao C Q, Gong W L, Chen M L, Li E R, Wang H, Xu W D and Han S S 2012 Appl. Phys. Lett. 101 141123
7 Dai H D, Gu G H, He W J, Ye L, Mao T Y and Chen Q 2016 Opt. Express 24 26080
8 Stantchev R I, Sun B Q, Hornett S M, Hobson P A, Gibson G M, Padgett M J and Hendry E 2016 Sci. Adv. 2 e1600190
9 Chandarana H, Feng L, Ream J, Wang A, Babb J S, Block K T, Sodickson D K and Otazo R 2015 Invest. Radiol. 50 749
10 Cand\`es E J and Tao T 2005 IEEE Trans. Inf. Theory 51 4203
11 Cand\`es E J 2008 CR MATH 346 589
12 Xu G W and Xu Z Q 2015 IEEE Trans. Inf. Theory 61 469
13 Ma Z, Zhang D G, Liu S, Song J J and Hou Y X 2016 Eng. Comput. 33 2448
14 Ke J and Lam E Y 2016 Opt. Express 24 9869
15 Sun M J, Meng L T, Edgar M P, Padgett M J and Radwell N 2017 Sci. Rep. 7 3464
16 Yu W K, Liu X F, Yao X R, Wang C, Zhai Y and Zhai G J 2014 Sci. Rep. 4 5834
17 Czajkowski K M, Pastuszczak A and Koty\`nski R 2018 Sci. Rep. 8 466
18 Liu X F, Yao X R, Wang C, Guo X Y and Zhai G J 2017 Opt. Express 25 3286
19 Stern A, Zeltzer Y and Rivenson Y 2013 J. Opt. Soc. Am. A 30 1069
20 Chen M L, Li E R and Han S S 2014 Appl. Opt. 53 2924
21 Zhou C, Huang H Y, Liu B and Song L J 2016 Acta Opt. Sin. 36 0911001
22 Li M F, Yan L, Yang R and Liu Y X 2019 Acta Phys. Sin. 68 064202 (in Chinese)
23 Zhang Z B, Ma X and Zhong J G 2015 Nat. Commun. 6 6225
24 Zhang Z B, Wang X Y, Zheng G A and Zhong J G 2017 Opt. Express 25 19619
25 Sun S, Liu W T, Lin H Z, Zhang E F, Liu J Y, Li Q and Chen P X 2016 Sci. Rep. 6 37013
26 Trinh C V, Dinh K Q, Nguyen V A and Jeon B Proceedings of the 22nd European Signal Processing Conference, September 1-5, 2014, Lisbon, Portugal, p. 231
[1] Ghost imaging based on the control of light source bandwidth
Zhao-Qi Liu(刘兆骐), Yan-Feng Bai(白艳锋), Xuan-Peng-Fan Zou(邹璇彭凡), Li-Yu Zhou(周立宇), Qin Fu(付芹), and Xi-Quan Fu(傅喜泉). Chin. Phys. B, 2023, 32(3): 034210.
[2] Iterative filtered ghost imaging
Shao-Ying Meng(孟少英), Mei-Yi Chen(陈美伊), Jie Ji(季杰), Wei-Wei Shi(史伟伟), Qiang Fu(付强), Qian-Qian Bao(鲍倩倩), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2022, 31(2): 028702.
[3] Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment
Shang-Qu Yan(颜上取), Han Zhang(张含), Bei Liu(刘备), Hao Tang(汤昊), and Sheng-You Qian(钱盛友). Chin. Phys. B, 2021, 30(2): 028704.
[4] Computational ghost imaging with deep compressed sensing
Hao Zhang(张浩), Yunjie Xia(夏云杰), and Deyang Duan(段德洋). Chin. Phys. B, 2021, 30(12): 124209.
[5] An image compressed sensing algorithm based on adaptive nonlinear network
Yuan Guo(郭媛), Wei Chen(陈炜), Shi-Wei Jing(敬世伟). Chin. Phys. B, 2020, 29(5): 054203.
[6] Compressed ghost imaging based on differential speckle patterns
Le Wang(王乐), Shengmei Zhao(赵生妹). Chin. Phys. B, 2020, 29(2): 024204.
[7] Super-resolution filtered ghost imaging with compressed sensing
Shao-Ying Meng(孟少英), Wei-Wei Shi(史伟伟), Jie Ji(季杰), Jun-Jie Tao(陶俊杰), Qian Fu(付强), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2020, 29(12): 128704.
[8] Influence of random phase modulation on the imaging quality of computational ghost imaging
Chao Gao(高超), Xiao-Qian Wang(王晓茜), Hong-Ji Cai(蔡宏吉), Jie Ren(任捷), Ji-Yuan Liu(刘籍元), Zhi-Hai Yao(姚治海). Chin. Phys. B, 2019, 28(2): 020201.
[9] Lensless two-color ghost imaging from the perspective of coherent-mode representation
Bin Luo(罗斌), Guohua Wu(吴国华), Longfei Yin(尹龙飞). Chin. Phys. B, 2018, 27(9): 094202.
[10] Compressed sensing sparse reconstruction for coherent field imaging
Bei Cao(曹蓓), Xiu-Juan Luo(罗秀娟), Yu Zhang(张羽), Hui Liu(刘 辉), Ming-Lai Chen(陈明徕). Chin. Phys. B, 2016, 25(4): 040701.
No Suggested Reading articles found!