Experimental Evaluation of Bayesian Image Reconstruction Combined with Spatial-Superresolution and Spectral Reflectance Recovery

Yusuke Murayama, Pengchang Zhang, Ari Ide-Ektessabi

Abstract

Acquisition of a multispectral image and analysis of the object based on spectral information recovered from the image has recently received attention in digital archiving of cultural assets. However multispectral imaging faces such problems as long image acquisition time and severe registration between band images. In order to solve them, we have proposed an extended method combining Bayesian image superresolution with spectral reflectance recovery. In this study we evaluated quantitatively the performance of the proposed technique using a typical 6-band multispectral scanner and a Japanese painting. The accuracy of recovered spectral reflectance was investigated with respect to the ratio of the capturing resolution to the recovering resolution. The experimental result indicated that the spatial resolution can be increased by around 1.7 times, which means image capturing time can be reduced almost by one third and besides the angle of view can be extended by 1.7 times.

References

  1. DiCarlo, J. M. and Wandell, B. a. (2003). Spectral estimation theory: beyond linear but before Bayesian. Journal of the Optical Society of America. A, Optics, image science, and vision, 20(7):1261-1270.
  2. Fukuda, H., Uchiyama, T., Haneishi, H., Yamaguchi, M., and Ohyama, N. (2005). Development of a 16-band multispectral image archiving system. Proceedings of SPIE, 5667:136-145.
  3. Murayama, Y. and Ide-Ektessabi, A. (2012). Application of bayesian image superresolution to spectral reflectance estimation. Optical Engineering, 51(11):111713.
  4. Pelagotti, A. and Mastio, A. D. (2008). Multispectral imaging of paintings. IEEE Signal Processing Magazine, 25(4):27-36.
  5. Shimano, N., Terai, K., and Hironaga, M. (2007). Recovery of spectral reflectances of objects being imaged by multispectral cameras. Journal of the Optical Society of America. A, Optics, image science, and vision, 24(10):3211-3219.
  6. Toque, J. A., Sakatoku, Y., and Ide-Ektessabi, A. (2009). Pigment identification by analytical imaging using multispectral images. Image Processing ICIP 2009 16th IEEE International Conference, pages 2861- 2864.
  7. Yamaguchi, M., Teraji, T., Ohsawa, K., Uchiyama, T., and Motomura, H. (2002). Color image reproduction based on multispectral and multiprimary imaging: experimental evaluation. Proceedings of SPIE, 4663:15- 26.
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Paper Citation


in Harvard Style

Murayama Y., Zhang P. and Ide-Ektessabi A. (2013). Experimental Evaluation of Bayesian Image Reconstruction Combined with Spatial-Superresolution and Spectral Reflectance Recovery . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 139-142. DOI: 10.5220/0004346001390142


in Bibtex Style

@conference{visapp13,
author={Yusuke Murayama and Pengchang Zhang and Ari Ide-Ektessabi},
title={Experimental Evaluation of Bayesian Image Reconstruction Combined with Spatial-Superresolution and Spectral Reflectance Recovery},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={139-142},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004346001390142},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Experimental Evaluation of Bayesian Image Reconstruction Combined with Spatial-Superresolution and Spectral Reflectance Recovery
SN - 978-989-8565-47-1
AU - Murayama Y.
AU - Zhang P.
AU - Ide-Ektessabi A.
PY - 2013
SP - 139
EP - 142
DO - 10.5220/0004346001390142