Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
Source abstract
Abstract
Compressive sensing theory has addressed the limitations of traditional methods in the field of information technology, and led to a revolution. On the basis of compressive sensing theory research, this study utilized the exterior determinacy and inherent randomness of chaotic sequences, designed a pseudo-random circulant measurement matrix based on chaotic sequence. Compared with other deterministic measurement matrices, the restoration effect of the designed measurement matrix remarkably improved and showed advantages in hardware and storage. Then, this study developed a single-pixel imaging scheme that could accurately obtain color information. The proposed improved measurement matrix combined with the hardware system could accurately reconstruct color images and had good robustness according to various experimental data.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sparse and Compressive Sensing Techniques · Blind Source Separation Techniques · Random lasers and scattering media
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Science and Engineering, Northeastern University, No.3-11, WenHua Road, Heping District, Shenyang 110819, China
Shenyang Institute of Automation Chinese Academy of Science, No.114, NanTa Street, ShenHe District, Shenyang 110016, China
Funding
National Natural Science Foundation of China
References (40)
Show source-provided references
1. Compressed sensing10.1109/TIT.2006.871582 ↗
2. Candès, E.J., and Romberg, J. (2006, January 8–11). Robust signal recovery from incomplete observations. Proceedings of the IEEE International Conference on Image Processing, Atlanta, GA, USA.10.1109/ICIP.2006.312579 ↗
3. Candès, E.J. (2006, January 22–30). Compressive sampling. Proceedings of the International Congress of Mathematicians, Madrid, Spain.
4. Stable signal recovery from incomplete and inaccurate measurements10.1002/cpa.20124 ↗
5. HYCA: A New Technique for Hyperspectral Compressive Sensing10.1109/TGRS.2014.2365534 ↗
6. Research on GF-1 remote sensing HIS image fusion algorithm based on compressed sensing
7. Analysis of infrared remote sensing image reconstruction algorithms based on compressive sensing
8. Guariglia, E. (2016). Entropy and Fractal Antennas. Entropy, 18.10.3390/e18030084 ↗
9. On the Weierstrass-Mandelbrot fractal function10.1098/rspa.1980.0044 ↗
10. Random Access Compressed Sensing over Fading and Noisy Communication Channels10.1109/TWC.2013.032013.120489 ↗
11. Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2008, January 23–28). Discriminative learned dictionaries for local image analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA.10.1109/CVPR.2008.4587652 ↗
12. Qin, J., and Guo, W.H. (2013, January 7–11). An efficient compressive sensing MR image reconstruction scheme. Proceedings of the IEEE, International Symposium on Biomedical Imaging, San Francisco, CA, USA.10.1109/ISBI.2013.6556473 ↗
13. Based on sparse vector and low-rank matrix compressed sensing magnetic resonance image reconstruction algorithm
14. A Survey of Magnetic Resonance Imaging Based on Compressed Sensing
15. Compressive sensing of high betweenness centrality nodes in networks10.1016/j.physa.2017.12.145 ↗
16. MEMS-based super-resolution remote sensing system using compressive sensing10.1016/j.optcom.2018.05.046 ↗
17. Grid cell firing field detection using compressed sensing. Biomed10.1016/j.bspc.2018.05.006 ↗
18. An image encryption algorithm based on chaotic system and compressive sensing10.1016/j.sigpro.2018.02.007 ↗
19. A new compressive imaging camera architecture using optical-domain compression
20. Single-pixel imaging via compressive sampling10.1109/MSP.2007.914730 ↗
21. Transmission Control for Compressive Sensing Video over Wireless Channel10.1109/TWC.2013.012313.121150 ↗
22. Infrared Camera Using a Single Nano-Photodetector10.1109/JSEN.2012.2225424 ↗
23. Terahertz imaging: Compressing onto a single pixel10.1038/nphoton.2014.167 ↗
24. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information10.1109/TIT.2005.862083 ↗
25. Compressive sampling with chaotic dynamical systems
26. Compressive sensing with chaotic sequence10.1109/LSP.2010.2052243 ↗
27. A Simple Proof of the Restricted Isometry Property for Random Matrices10.1007/s00365-007-9003-x ↗
28. Bajwa, W.U., Haupt, J.D., Raz, G.M., Wright, S.J., and Nowak, R.D. (2007, January 26–29). Toeplitz-structured compressed sensing matrices. Proceedings of the 14th IEEE/SP Workshop on Statistical Signal Processing, Madison, WI, USA.10.1109/SSP.2007.4301266 ↗
29. An introduction to compressive sampling10.1109/MSP.2007.914731 ↗
30. Deterministic Sensing Matrices in Compressive Sensing: A Survey10.1155/2013/192795 ↗
31. A performance comparison of measurement matrices in compressive sensing10.1002/dac.3576 ↗
32. Compressive sensing algorithms for signal processing applications: A survey
33. Image Quality Assessing by Combing PSNR with SSIM
34. Bayesian compressive sensing10.1109/TSP.2007.914345 ↗
35. Ajoune, Y., Kaabouch, N., el Ghazi, H., and Tamtaoui, A. (2017, January 9–11). Compressive sensing: A performance comparison of recovery algorithm. Proceedings of the Computing and Communication Workshop and Conference, Las Vegas, NV, USA.10.1109/CCWC.2017.7868430 ↗
36. Bayesian compressive sensing using Laplace priors10.1109/TIP.2009.2032894 ↗
37. Bayesian compressive sensing via belief propagation10.1109/TSP.2009.2027773 ↗
38. Signal recovery from random measurements via orthogonal matching pursuit10.1109/TIT.2007.909108 ↗
39. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples10.1016/j.acha.2008.07.002 ↗
40. Li, C. (2009). An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing. [Ph.D. Thesis, Rice University].