The Visual Computer

, Volume 29, Issue 6–8, pp 773–783 | Cite as

Interactive chromaticity mapping for multispectral images

  • Yanxiang Lan
  • Jiaping Wang
  • Stephen Lin
  • Minmin Gong
  • Xin Tong
  • Baining Guo
Original Article

Abstract

Multispectral images record detailed color spectra at each image pixel. To display a multispectral image on conventional output devices, a chromaticity mapping function is needed to map the spectral vector of each pixel to the displayable three dimensional color space. In this paper, we present an interactive method for locally adjusting the chromaticity mapping of a multispectral image. The user specifies edits to the chromaticity mapping via a sparse set of strokes at selected image locations and wavelengths, then our method automatically propagates the edits to the rest of the multispectral image. The key idea of our approach is to factorize the multispectral image into a component that indicates spatial coherence between different pixels, and one that describes spectral coherence between different wavelengths. Based on this factorized representation, a two-step algorithm is developed to efficiently propagate the edits in the spatial and spectral domains separately. The method presented provides photographers with efficient control over color appearance and scene details in a manner not possible with conventional color image editing. We demonstrate the use of interactive chromaticity mapping in the applications of color stylization to emulate the appearance of photographic films, enhancement of image details, and manipulation of different light transport effects.

Keywords

Multispectral imaging Chromaticity mapping Edit propagation 

Supplementary material

(AVI 20.0 MB)

References

  1. 1.
    Alien Skin Software, LLC.: Exposure4. http://www.alienskin.com/exposure/index.aspx
  2. 2.
    An, X., Pellacini, F.: Appprop: all-pairs appearance-space edit propagation. In: SIGGRAPH ’08: ACM SIGGRAPH 2008 Papers, pp. 1–9. ACM, New York (2008). doi:10.1145/1399504.1360639 Google Scholar
  3. 3.
    Cao, X., Tong, X., Dai, Q., Lin, S.: High resolution multispectral video capture with a hybrid camera system. In: Proc. of Comp. Vis. and Pattern Rec. (CVPR) (2009) Google Scholar
  4. 4.
    Carroll, J., Neitz, J., Neitz, M.: Estimates of lm cone ratio from erg flicker photometry and genetics. J. Vis. 2((8):1), 531–542 (2002) Google Scholar
  5. 5.
    Chakrabarti, A., Zickler, T.: Statistics of real-world hyperspectral images. In: IEEE Int. Conf. Comp. (2011) Google Scholar
  6. 6.
    Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: Computer Graphics Proceedings, Annual Conference Series. Proceedings of SIGGRAPH, vol. 97, pp. 369–378 (1997) Google Scholar
  7. 7.
    Descour, M.R., Dereniak, E.L.: Computed-tomography imaging spectrometer: experimental calibration and reconstruction results. Appl. Opt. 34(22), 4817–4826 (1995) CrossRefGoogle Scholar
  8. 8.
    Du, H., Tong, X., Cao, X., Lin, S.: A prism-based system for multispectral video acquisition. In: Proc. of Int’l Conf. on Comp. Vis. (ICCV) (2009) Google Scholar
  9. 9.
    Farbman, Z., Fattal, R., Lischinski, D.: Diffusion maps for edge-aware image editing. ACM Trans. Graph. 29(6), 145:1–145:10 (2010). http://doi.acm.org/10.1145/1882261.1866171 CrossRefGoogle Scholar
  10. 10.
    Galatsanos, N., Segall, A., Katsaggelos, A.: Digital Image Enhancement. Encycl. Optical Engineering (2005) Google Scholar
  11. 11.
    Gat, N.: Imaging spectroscopy using tunable filters: a review. In: SPIE Wavelet Appl. VII, vol. 4056, pp. 50–64 (2000) CrossRefGoogle Scholar
  12. 12.
    Gehm, M.E., John, R., Brady, D.J., Willett, R., Schultz, T.: Single-shot compressive spectral imaging with a dual-disperser architecture. Opt. Express 15(21), 14013–14027 (2007) CrossRefGoogle Scholar
  13. 13.
    Jolliffe, I.: Principal Component Analysis. Springer Series in Statistics. Springer, Berlin (2002). http://books.google.com/books?id=_olByCrhjwIC MATHGoogle Scholar
  14. 14.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graph. 23(3), 689–694 (2004) (SIGGRAPH 2004) CrossRefGoogle Scholar
  15. 15.
    Li, Y., Ju, T., Hu, S.M.: Instant propagation of sparse edits on images and videos. Comput. Graph. Forum, 2049–2054 (2010) Google Scholar
  16. 16.
    Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph. 25(3), 646–653 (2006). http://doi.acm.org/10.1145/1141911.1141936 CrossRefGoogle Scholar
  17. 17.
    Mohan, A., Raskar, R., Tumblin, J.: Agile spectrum imaging: programmable wavelength modulation for cameras and projectors. Comput. Graph. Forum 27(2), 709–717 (2008) CrossRefGoogle Scholar
  18. 18.
    Mooney, J.M., Vickers, V.E., An, M., Brodzik, A.K.: High-throughput hyperspectral infrared camera. J. Opt. Soc. Am. A 14(11), 2951–2961 (1997) CrossRefGoogle Scholar
  19. 19.
    Ng, R., Ramamoorthi, R., Hanrahan, P.: All-frequency shadows using non-linear wavelet lighting approximation. ACM Trans. Graph. 22(3), 376–381 (2003) CrossRefGoogle Scholar
  20. 20.
    Park, J., Lee, M., Grossberg, M.D., Nayar, S.K.: Multispectral imaging using multiplexed illumination. In: Proc. of Int’l Conf. on Comp. Vis. (ICCV) (2007) Google Scholar
  21. 21.
    Peers, P., Mahajan, D.K., Lamond, B., Ghosh, A., Matusik, W., Ramamoorthi, R., Debevec, P.: Compressive light transport sensing. ACM Trans. Graph. 28(1), 3:1–3:18 (2009) CrossRefGoogle Scholar
  22. 22.
    Pellacini, F., Lawrence, J.: Appwand: editing measured materials using appearance-driven optimization. ACM Trans. Graph. 26(3) (2007). http://doi.acm.org/10.1145/1276377.1276444
  23. 23.
    Schechner, Y.Y., Nayar, S.K.: Generalized mosaicing: wide field of view multispectral imaging. IEEE Trans. Pattern Anal. Mach. Intell. 24(10), 1334–1348 (2002) CrossRefGoogle Scholar
  24. 24.
    Vandervlugt, C., Masterson, H., Hagen, N., Dereniak, E.L.: Reconfigurable liquid crystal dispersing element for a computed tomography imaging spectrometer. Proc. SPIE 6565 (2007) Google Scholar
  25. 25.
    Wagadarikar, A., John, R., Willett, R., Brady, D.J.: Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt. 47(10), B44–B51 (2008) CrossRefGoogle Scholar
  26. 26.
    Wandell, B.A., Silverstein, L.D.: The Science of Color, 2nd edn., Chap. Digital Color Reproduction. Opt. Soc. Am., Washington (2003) Google Scholar
  27. 27.
    Wang, J., Dong, Y., Tong, X., Lin, Z., Guo, B.: Kernel Nyström method for light transport. ACM Trans. Graph. 28(3), 29:1–29:10 (2009) (SIGGRAPH 2009) Google Scholar
  28. 28.
    Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley-Interscience, New York (2000) Google Scholar
  29. 29.
    Xu, K., Li, Y., Ju, T., Hu, S.M., Liu, T.Q.: Efficient affinity-based edit propagation using k-d tree. ACM Trans. Graph. 28, 118:1–118:6 (2009). doi:10.1145/1618452.1618464. http://doi.acm.org/10.1145/1618452.1618464 Google Scholar
  30. 30.
    Xu, K., Wang, J., Tong, X., Hu, S.M., Guo, B.: Edit propagation on bidirectional texture functions. Comput. Graph. Forum 28(7), 1871–1877 (2009) CrossRefGoogle Scholar
  31. 31.
    Yamaguchi, M., Haneishi, H., Fukuda, H., Kishimoto, J., Kanazawa, H., Tsuchida, M., Iwama, R., Ohyama, N.: High-fidelity video and still-image communication based on spectral information: natural vision system and its applications. In: SPIE/IS&T Electr. Imaging, vol. 6062 (2006) Google Scholar
  32. 32.
    Yasuma, F., Mitsunaga, T., Iso, D., Nayar, S.: Generalized assorted pixel camera: post-capture control of resolution, dynamic range and spectrum. Tech. rep. (2008) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yanxiang Lan
    • 1
    • 2
  • Jiaping Wang
    • 3
  • Stephen Lin
    • 2
  • Minmin Gong
    • 2
  • Xin Tong
    • 2
  • Baining Guo
    • 1
    • 2
  1. 1.Tsinghua UniversityBeijingChina
  2. 2.Microsoft Research AsiaBeijingChina
  3. 3.Microsoft CorporationRedmondUSA

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