CMOS low data rate imaging method based on compressed sensing

Optics and Laser Technology

Volumn 44, Issue 5, 2012, Pages 1338-1345

  Xiao, Long Long ^a   Liu, Kun ^a   Han, Da Peng ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Complementary metal oxide semiconductor imaging; Compressed sensing; Image compression

Indexed keywords

BASIS SETS; COMPLEMENTARY METAL OXIDE SEMICONDUCTORS; COMPRESSED SENSING; ELECTRICAL CURRENT; FLOATING-GATES; HARDWARE CIRCUITS; IMAGING METHOD; IMAGING QUALITY; INTEGRATION OF IMAGES; LOW DATA RATES; NOVEL DESIGN; OPTIMIZATION ALGORITHMS; REPROGRAMMABLE; TOTAL VALUES; VECTOR-MATRIX MULTIPLIERS;

ALGORITHMS; COMPRESSION RATIO (MACHINERY); IMAGE COMPRESSION; SIGNAL RECONSTRUCTION;

DIGITAL IMAGE STORAGE;

EID: 84857039647     PISSN: 00303992     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.optlastec.2011.12.033     Document Type: Article

References (25)

1. D.L. Donoho Compressed Sensing IEEE Transactions On Information Theory 52 4 2006 1289 1306
2. JN Laska, S Kirolos, MF Duarte, TS Ragheb, RG Baraniuk, Y Massoud, Theory and implementation of an analog-to-information converter using random demodulation, In: Proceedings of IEEE international symposium on circuits and systems; 2007. p. 195962.
3. R Baraniuk, S Philippe, Compressive Radar Imaging, in: Proceedings of 2007 IEEE radar conference; 2007. p. 12833.
4. J.W. Ma Single-pixel remote sensing IEEE Geoscience and Remote Sensing Letters 6 2 2009 199 203
5. L. Michael, D.L. Donoho, and M.P. John Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging Magnetic Resonance In Medicine 58 6 2007 1182 1195
6. J. Bobin, J.L. Starck, and R. Ottensamer Compressed Sensing in Astronomy IEEE Journal of Selected Topics in Signal Processing 28 2 2009 609 618
7. M. Duarte, M. Davenport, D. Takhar, J. Laska, T. Sun, and K. Kelly Single-pixel imaging via compressive sampling IEEE Signal Processing Magazine 25 2 2008 83 91
8. K. Aizawa, Y. Egi, and T. Hamamoto Computational image sensor for on sensor compression IEEE Transactions on Electron Devices 44 10 1997 1724 1730
9. K. Aizawa, H. Ohno, and Y. Egi On sensor image compression IEEE Transactions on Circuits and Systems for Video Technology 7 3 1997 543 548
10. Shoji Kawahito, Yoshiaki Tadokoro, and Akira Matsuzawa A CMOS Image Sensor with Analog Two-Dimensional DCT-Based Compression Circuits for One-Chip Cameras IEEE Journal of Solid-state Circuits 32 12 1997 2030 2041
11. Ryan Robucci, Leung Kin Chiu, and Paul Hasler Compressive Sensing on a CMOS Separable Transform Image Sensor The Proceedings of the IEEE 98 6 2010 1089 1101
12. E.J. Candes The restricted isometry property and its implications for compressed sensing Compte Rendus de lAcademie des Sciences 346 1 2006 589 592
13. R. Baraniuk A Lecture on Compressive Sensing IEEE Signal Processing Magazine 24 4 2007 118 121
14. E.J. Candes, and T. Tao Decoding by linear programming IEEE Transaction On Information Theory 51 12 2005 4203 4215
15. E.J. Candes, J. Romberg, and T. Tao Stable signal recovery from incomplete and inaccurate measurements Communications On Pure And Applied Mathematics 59 8 2006 1207 1223
16. D.L. Donoho For most large underdetermined systems of linear equations, the minimal L1 norm solution is also the sparsest solution Communications On Pure And Applied Mathematics 59 6 2006 797 829
17. J. Tropp, and A. Gilbert Signal recovery from random measurements via orthogonal matching pursuit Transactions on Information Theory 53 12 2007 4655 4666
18. K Herrity, A Gilbert, JA Tropp, Sparse approximation via iterative thresholding, in: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Washington DC, USA; 2006. p. 6247.
19. E.J. Candes, J. Romberg, and T. Tao Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information IEEE Transactions on Information Theory 52 2 2006 489 509
20. D. Litwiller CCD vs. CMOS: facts and fiction Photonics Spectra 35 1 2001 154 158
21. Ryan Robucci, Jordan Gray, David Abramson, Paul E Hasler, A 256×256 separable transform CMOS imager. IEEE international symposium on circuits and system; 2008 p. 14203.
22. Ravi Chawla, Abhishek Bandyopadhyay, Venkatesh Srinivasan, and Paul Haslert A 531 nw/mhz, 128×32 current-mode programmable analog vector-matrix multiplier with over two decades of linearity IEEE Custom Integrated Circuits Conference 2004 651 654
23. Yohan Joly, Laurent Lopez, Jean-Michel Portal, Hassen Aziza, Yannick Bert, Franck Julien,et al., Impact of hump effect on MOSFET mismatch in the sub-threshold area for low power analog applications. (ICSICT) IEEE international conference on digital object identifier; 201). p. 18179.
24. PR. Kinget Device mismatch and tradeoffs in the design of analog circuits IEEE Journal of Solid-State Circuits 40 6 2005 1212 1224
25. A Fish, O Yadid-Pecht, Circuits at the nanoscale: communications, imaging, and sensing, ISBN: 978-1-4200-7062-0; 2008. p.45881.