Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 125321, 7 pages
http://dx.doi.org/10.1155/2012/125321
Research Article

Self-Adaptive Image Reconstruction Inspired by Insect Compound Eye Mechanism

1College of Computer and Information Engineering, Hohai University, Nanjing, Jiangsu 211100, China
2College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China

Received 23 November 2012; Accepted 17 December 2012

Academic Editor: Sheng-yong Chen

Copyright © 2012 Jiahua Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. C. Kenneth and R. E. Woods, Digital Image Processing, Publishing House of Electronics Industry, Beijing, China, 2002.
  2. F. G. B. D. Natale, G. S. Desoli, and D. D. Giusto, “Adaptive least-squares bilinear interpolation (ALSBI): a new approach to image-data compression,” Electronics Letters, vol. 29, no. 18, pp. 1638–1640, 1993. View at Google Scholar · View at Scopus
  3. L. Chen and C. M. Gao, “Fast discrete bilinear interpolation algorithm,” Computer Engineering and Design, vol. 28, p. 15, 2007. View at Google Scholar
  4. S. Y. Chen and Z. J. Wang, “Acceleration strategies in generalized belief propagation,” IEEE Transactions on Industrial Informatics, vol. 8, p. 1, 2012. View at Google Scholar
  5. N. M. Kwok, X. P. Jia, D. Wang et al., “Visual impact enhancement via image histogram smoothing and continuous intensity relocation,” Computers & Electrical Engineering, vol. 37, p. 5, 2011. View at Google Scholar
  6. L. Z. Xu, M. Li, A. Y. Shi et al., “Feature detector model for multi-spectral remote sensing image inspired by insect visual system,” Acta Electronica Sinica, vol. 39, p. 11, 2011. View at Google Scholar
  7. F. C. Huang, M. Li, A. Y. Shi et al., “Insect visual system inspired small target detection for multi-spectral remotely sensed images,” Journal on Communications, vol. 32, p. 9, 2011. View at Google Scholar
  8. H. Schiff, “A discussion of light scattering in the Squilla rhabdom,” Kybernetik, vol. 14, no. 3, pp. 127–134, 1974. View at Google Scholar · View at Scopus
  9. B. Dore, H. Schiff, and M. Boido, “Photomechanical adaptation in the eyes of Squilla mantis (Crustacea, Stomatopoda),” Italian Journal of Zoology, vol. 72, no. 3, pp. 189–199, 2005. View at Google Scholar · View at Scopus
  10. B. Greiner, “Adaptations for nocturnal vision in insect apposition eyes,” International Review of Cytology, vol. 250, pp. 1–46, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Horridge, “The spatial resolutions of the apposition compound eye and its neuro-sensory feature detectors: observation versus theory,” Journal of Insect Physiology, vol. 51, no. 3, pp. 243–266, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Ikeno, “A reconstruction method of projection image on worker honeybees' compound eye,” Neurocomputing, vol. 52–54, pp. 561–566, 2003. View at Google Scholar · View at Scopus
  13. J. Gál, T. Miyazaki, and V. B. Meyer-Rochow, “Computational determination of refractive index distribution in the crystalline cones of the compound eye of Antarctic krill (Euphausia superba),” Journal of Theoretical Biology, vol. 244, no. 2, pp. 318–325, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Y. Chen, H. Tong, Z. Wang, S. Liu, M. Li, and B. Zhang, “Improved generalized belief propagation for vision processing,” Mathematical Problems in Engineering, vol. 2011, Article ID 416963, 12 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. V. Cevher, P. Indyk, L. Carin, and R. Baraniuk, “Sparse signal recovery and acquisition with graphical models,” IEEE Signal Processing Magazine, vol. 27, no. 6, pp. 92–103, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. M. F. Duarte and R. G. Baraniuk, “Spectral compressive sensing,” IEEE Transactions on Signal Processing, vol. 6, 2011. View at Google Scholar
  17. L. Z. Xu, X. F. Ding, X. Wang, G. F. Lv, and F. C. Huang, “Trust region based sequential quasi-Monte Carlo filter,” Acta Electronica Sinica, vol. 39, no. 3, pp. 24–30, 2011. View at Google Scholar · View at Scopus
  18. J. Treichler and M. A. Davenport, “Dynamic range and compressive sensing acquisition receivers,” in Proceedings of the Defense Applications of Signal Processing (DASP '11), 2011.
  19. S. Y. Chen and Y. F. Li, “Determination of stripe edge blurring for depth sensing,” IEEE Sensors Journal, vol. 11, no. 2, pp. 389–390, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Y. Chen, Y. F. Li, and J. Zhang, “Vision processing for realtime 3-D data acquisition based on coded structured light,” IEEE Transactions on Image Processing, vol. 17, no. 2, pp. 167–176, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Hegde and R. G. Baraniuk, “Sampling and recovery of pulse streams,” IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1505–1517, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Y. Shi, L. Z. Xu, and F. Xu, “Multispectral and panchromatic image fusion based on improved bilateral filter,” Journal of Applied Remote Sensing, vol. 5, Article ID 053542, 2011. View at Google Scholar
  23. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. E. J. Candès, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Communications on Pure and Applied Mathematics, vol. 59, no. 8, pp. 1207–1223, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?” IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406–5425, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Z. Xu, X. F. Li, and S. X. Yang, “Wireless network and communication signal processing,” Intelligent Automation & Soft Computing, vol. 17, pp. 1019–1021, 2011. View at Google Scholar
  28. D. Takhar, J. N. Laska, M. B. Wakin et al., “A new compressive imaging camera architecture using optical-domain compression,” in Computational Imaging IV, vol. 6065 of Proceedings of SPIE, January 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. D. Baron, B. Wakin, and S. Sarvotham, “Distributed Compressed Sensing,” Rice University, 2006.
  30. D. Baron and M. F. Duarte, “An information-theoretic approach to distributed compressed sensing,” in Proceedings of the Allerton Conference on Communication, Control, and Computing, vol. 43, Allerton, Ill, USA, 2005.
  31. D. Baron, M. F. Duarte, S. Sarvotham, M. B. Wakin, and R. G. Baraniuk, “Distributed compressed sensing of jointly sparse signals,” in Proceedings of the 39th Asilomar Conference on Signals, Systems and Computers, pp. 1537–1541, November 2005. View at Scopus
  32. M. B. Wakin, S. Sarvotham, and M. F. Duarte, “Recovery of jointly sparse signals from few random projections,” in Proceedings of the Workshop on Neural Information Proccessing Systems, 2005.
  33. S. Chen, Y. Zheng, C. Cattani, and W. Wang, “Modeling of biological intelligence for SCM system optimization,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 769702, 10 pages, 2012. View at Publisher · View at Google Scholar
  34. C. Cattani, S. Y. Chen, and G. Aldashev, “Information and modeling in complexity,” Mathematical Problems in Engineering, vol. 2012, Article ID 868413, 3 pages, 2012. View at Publisher · View at Google Scholar
  35. S. Y. Chen and X. L. Li, “Functional magnetic resonance imaging for imaging neural activity in the human brain: the annual progress,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 613465, 9 pages, 2012. View at Publisher · View at Google Scholar
  36. C. Cattani, “On the existence of wavelet symmetries in Archaea DNA,” Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 673934, 21 pages, 2012. View at Publisher · View at Google Scholar
  37. X. H. Wang, M. Li, and S. Chen, “Long memory from Sauerbrey equation: a case in coated quartz crystal microbalance in terms of ammonia,” Mathematical Problems in Engineering, vol. 2011, Article ID 758245, 9 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus