OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 9 — Mar. 20, 2014
  • pp: 1918–1928

Super-resolution fusion of complementary panoramic images based on cross-selection kernel regression interpolation

Lidong Chen, Anup Basu, Maojun Zhang, Wei Wang, and Yu Liu  »View Author Affiliations


Applied Optics, Vol. 53, Issue 9, pp. 1918-1928 (2014)
http://dx.doi.org/10.1364/AO.53.001918


View Full Text Article

Enhanced HTML    Acrobat PDF (1064 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

© 2014 Optical Society of America

OCIS Codes
(100.6640) Image processing : Superresolution
(110.4190) Imaging systems : Multiple imaging
(350.2660) Other areas of optics : Fusion
(110.3010) Imaging systems : Image reconstruction techniques

ToC Category:
Image Processing

History
Original Manuscript: October 30, 2013
Revised Manuscript: January 29, 2014
Manuscript Accepted: February 1, 2014
Published: March 19, 2014

Virtual Issues
Vol. 9, Iss. 5 Virtual Journal for Biomedical Optics

Citation
Lidong Chen, Anup Basu, Maojun Zhang, Wei Wang, and Yu Liu, "Super-resolution fusion of complementary panoramic images based on cross-selection kernel regression interpolation," Appl. Opt. 53, 1918-1928 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-9-1918


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. K. Ikeuchi, M. Sakauchi, H. Kawasaki, and I. Sato, “Constructing virtual cities by using panoramic images,” Int. J. Comput. Vis. 58, 237–247 (2004). [CrossRef]
  2. M. Lhuillier, “Automatic scene structure and camera motion using a catadioptric system,” Comput. Vis. Image Underst. 109, 186–203 (2008). [CrossRef]
  3. W. Chen, I. Cheng, Z. Xiong, A. Basu, and M. Zhang, “A 2-point algorithm for 3D reconstruction of horizontal lines from a single omni-directional image,” Pattern Recogn. Lett. 32, 524–531 (2011). [CrossRef]
  4. M. Fiala and A. Basu, “Robot navigation using panoramic tracking,” Pattern Recogn. 37, 2195–2215 (2004). [CrossRef]
  5. H. M. Becerra, G. López-Nicolás, and C. Sagüés, “Omnidirectional visual control of mobile robots based on the 1D trifocal tensor,” Robot. Auton. Syst. 58, 796–808 (2010). [CrossRef]
  6. T. E. Boult, X. Gao, R. Micheals, and M. Eckman, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004). [CrossRef]
  7. C. H. Chen, Y. Yao, D. Page, B. Abidi, A. Koschan, and M. Abidi, “Heterogeneous fusion of omnidirectional and PTZ cameras for multiple object tracking,” IEEE Trans. Circuits Syst. Video Technol. 18, 1052–1063 (2008). [CrossRef]
  8. J. Gaspar, C. Decco, J. Okamoto, and J. S. Victor, “Constant resolution omnidirectional cameras,” in Proceedings of the 3rd Workshop on Omnidirectional Vision (2002), pp. 27–34.
  9. J. S. Chahl and M. V. Srinivasan, “Reflective surfaces for panoramic imaging,” Appl. Opt. 36, 8275–8285 (1997). [CrossRef]
  10. J. Zeng and X. Su, “A distortionless catadioptric panoramic imaging system for cylindrical scene,” Opto-Electron. Eng. 30, 42–45 (2003).
  11. R. A. Hicks and R. Bajcsy, “Catadioptric sensors that approximate wide-angle perspective projections,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2000), pp. 545–551.
  12. G. Stefan, “Mirror design for an omnidirectional camera with a uniform cylindrical projection when using the SVAVISCA sensor,” Research Reports of CMP (Czech Technical University in Prague, 2001).
  13. H. Nagahara, Y. Yagi, and M. Yachida, “Resolution improving method from multi-focal omnidirectional images,” in Proceedings of the IEEE International Conference on Image Processing (2001), pp. 654–657.
  14. H. Nagahara, Y. Yagi, and M. Yachida, “Super-resolution modeling,” in Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (2003), pp. 215–221.
  15. H. Nagahara, Y. Yagi, and M. Yachida, “Superresolution modeling using an omnidirectional image sensor,” IEEE Trans. Syst. Man Cybern. 33, 607–615 (2003). [CrossRef]
  16. Q. Peng and Y. Jia, “Wavelet-based resolution enhancement of omnidirectional images,” Acta Electron. Sin. 32, 1875–1879 (2004).
  17. C. Gao, H. Hua, and N. Ahuja, “A hemispherical imaging camera,” Comput. Vis. Image Underst. 114, 168–178 (2010). [CrossRef]
  18. L. Chen, W. Wang, M. Zhang, and W. Chen, “Design analysis of a complementary double-mirror catadioptric omnidirectional imaging system,” Acta Opt. Sin. 30, 3487–3494 (2010). [CrossRef]
  19. L. Chen, W. Wang, M. Zhang, W. Bao, and X. Zhang, “Complementary-structure catadioptric omnidirectional sensor design for resolution enhancement,” Opt. Eng. 50, 033201 (2011). [CrossRef]
  20. L. Chen, J. Lou, M. Zhang, W. Wang, and Y. Liu, “Fusion of complementary catadioptric panoramic images based on nonsubsampled contourlet transform,” Opt. Eng. 50, 127002 (2011). [CrossRef]
  21. H. Takeda, S. Farsiu, and P. Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Process. 16, 349–366 (2007). [CrossRef]
  22. H. Chang and D. Yeung, “Kernel-based distance metric learning for content-based image retrieval,” Image Vis. Comput. 25, 695–703 (2007). [CrossRef]
  23. P. H. Gosselin, F. Precioso, and S. Philipp-Foliguet, “Incremental kernel learning for active image retrieval without global dictionaries,” Pattern Recogn. 44, 2244–2254 (2011) http://www.sciencedirect.com/science/article/pii/S0031320310005716 . [CrossRef]
  24. H. Zhang, J. Yang, Y. Zhang, and T. S. Huang, “Non-local kernel regression for image and video restoration,” in Proceedings of the 11th European Conference on Computer Vision (2010), pp. 566–579.
  25. R. Saface-Rad, I. Tchoukanov, K. C. Smith, and B. Benhabib, “Three-dimensional location estimation of circular features for machine vision,” IEEE Trans. Robot. Autom. 8, 624–640 (1992). [CrossRef]
  26. T. Mashita, Y. Iwai, and M. Yachida, “Calibration method for misaligned catadioptric camera,” IEICE Trans. Inf. Syst. E89-D, 1984–1993 (2006). [CrossRef]
  27. G. Qu, D. Zhang, and P. Yan, “Information measure for performance of image fusion,” Electron. Lett. 38, 313–315 (2002). [CrossRef]
  28. C. S. Xydeas and V. Petrovic, “Objective image fusion performance measure,” Electron. Lett. 36, 308–309 (2000). [CrossRef]
  29. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error measurement to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited