Computational Spectral Imaging Based on Adaptive Spectral Imaging

  • Francisco H. Imai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7786)


This paper presents a new paradigm on adaptive spectral imaging to address practical spectral imaging issues such as robustness of spectral estimation transform, dependency on training sample set and impact of non-uniform illumination on estimation accuracy. Computational spectral imaging using reconfigurable imaging sensors with tunable spectral sensitivities is introduced as a possible powerful approach to address these practical spectral reconstruction issues. As an example of effectiveness of reconfigurable imaging sensor embodiment, experiments and results previously presented at the IS&T/SID 19th Color and Imaging Conference is reviewed mentioning potential applications and implementations of proposed computational spectral imaging.


Spectral imaging adaptive imaging tunable imaging sensors machine learning SVM 


  1. 1.
    Tominaga, S.: Spectral imaging by a multichannel camera. J. Electron Imaging 8, 332–341 (1999)CrossRefGoogle Scholar
  2. 2.
    Shaw, G., Burke, H.: Spectral imaging for remote sensing. Lincoln Laboratory Journal 14, 3–28 (2003)Google Scholar
  3. 3.
    Berns, R.S.: Color-accurate image archives using spectral imaging. In: Scientific Examination of Art: Modern Techniques in Conservation and Analysis, pp. 105–119. National Academies Press (2005)Google Scholar
  4. 4.
    Lin, A., Imai, F.H.: Efficient spectral imaging based on imaging systems with scene adaptation using tunable color pixels. In: Proc. of IS&T/SID Color and Imaging Conference, pp. 332–338 (2011)Google Scholar
  5. 5.
    Mohan, A., Raskar, R., Tumblin, J.: Agile Spectrum Imaging: Programmable wavelength modulation for cameras and projectors. Computer Graphics Forum 27, 709–717 (2008)CrossRefGoogle Scholar
  6. 6.
    Hauta-Kasari, M., Miyazawa, K., Toyooka, S., Parkkinen, J.: Spectral Vision System for Measuring Color Images. J. Opt. Soc. Am. A 16, 2352–2362 (1999)CrossRefGoogle Scholar
  7. 7.
    Nischan, M.L., Joseph, R.M., Libby, J.C., Kekeres, J.P.: Active spectral imaging. Lincoln Laboratory Journal 14, 131–143 (2003)Google Scholar
  8. 8.
    Imai, F., Taplin, L., Day, E.: Comparison of the accuracy of various transformations from multi-band images to reflectance spectra. Munsell Color Science Laboratory Technical Report, (2002),
  9. 9.
    Farrell, J.E., Xiao, F., Catrysse, P., Wandell, B.: A simulation tool for evaluating digital camera image quality, A simulation tool for evaluating digital camera image quality. In: Proc. SPIE, vol. 5294, pp. 124–131 (2004)Google Scholar
  10. 10.
    Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., Lin, C.-J.: LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)zbMATHGoogle Scholar
  11. 11.
    Nascimento, S.M.C., Ferreira, F., Foster, D.H.: Statistics of spatial cone-excitation ra-tios in natural scenes. J. Opt. Soc. Am. A 19, 1484–1490 (2002)CrossRefGoogle Scholar
  12. 12.
    Foster, D.H., Nascimento, S.M.C., Amano, K.: Information limits on neural identification of coloured surfaces in natural scenes. Visual Neurosci. 21, 331–336 (2004)CrossRefGoogle Scholar
  13. 13.
    Miao, L., Qi, H., Ramanath, R., Snyder, W.: Binary Tree-based Generic Demosaicking Algorithm for Multispectral Filter Arrays. IEEE T. Image Process. 15, 3350–3558 (2006)CrossRefGoogle Scholar
  14. 14.
    Zhang, X., Wandell, B.: A spatial extension of CIELAB for digital color reproduction. In: Proc. Soc. of. Inform. Display 1996 Digest, pp. 731–734 (1996)Google Scholar
  15. 15.
    Fairman, H.: Metameric Correction Using Parameric Decomposition. Color Res. App. 12, 261–265 (1987)CrossRefGoogle Scholar
  16. 16.
    Imai, F.: Image sensor compensation, US Patent Application 20120206631 (2012) Google Scholar
  17. 17.
    Longoni, A., Zaraga, F., Langfelder, G., Bombelli, L.: The Transverse Field Detector (TFD): A Novel Color-Sensitive CMOS Device. IEEE Electron Device Lett. 29, 1306–1308 (2008)CrossRefGoogle Scholar
  18. 18.
    Langfelder, G., Zaraga, F., Longoni, A.: Tunable Spectral Responses in a Color-Sensitive CMOS Pixel for Imaging Applications. IEEE Trans. Electron Devices 56, 2563–2569 (2009)CrossRefGoogle Scholar
  19. 19.
    Langfelder, G.: Design of a fully CMOS compatible 3-μm size color pixel. Microelectron. Reliab. 50, 163–173 (2010)CrossRefGoogle Scholar
  20. 20.
    Zaraga, F., Langfelder, G.: White balance by tunable spectral responsivities. J. Opt. Soc. Am A 27, 31–39 (2010)CrossRefGoogle Scholar
  21. 21.
    Zaraga, F., Langfelder, G., Longoni, A.: Implementation of an Interleaved Image sensor by means of the filterless Transverse Field Detector (TFD). J. Electron Imaging 19, 033013 (2010)Google Scholar
  22. 22.
    Langfelder, G., Longoni, A.F., Zaraga, F.: Implementation of a multi-spectral color im-aging device without color filter array. In: Proc. SPIE 7876, Digital Photography VII, p. 787608 (2011)Google Scholar
  23. 23.
    Miller, D.: Methods for adaptive spectral, spatial and temporal sensing for imaging applications, U.S. Patent 6,466,961 (October 15, 2002)Google Scholar
  24. 24.
    Sajadi, B., Majumder, A., Hiwada, K., Maki, A., Raskar, R.: Switchable Primaries Using Shiftable Layers of Color Filter Arrays. ACM Trans. Graph 30(4), Article 65 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francisco H. Imai
    • 1
  1. 1.Innovation CenterCanon U.S.A. Inc.San JoseUSA

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