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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 10 — May. 7, 2012
  • pp: 10658–10673

Hyperspectral video restoration using optical flow and sparse coding

Ajmal Mian and Richard Hartley  »View Author Affiliations

Optics Express, Vol. 20, Issue 10, pp. 10658-10673 (2012)

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Hyperspectral video acquisition is a trade-off between spectral and temporal resolution. We present an algorithm for recovering dense hyperspectral video of dynamic scenes from a few measured multispectral bands per frame using optical flow and sparse coding. Different set of bands are measured in each video frame and optical flow is used to register them. Optical flow errors are corrected by exploiting sparsity in the spectra and the spatial correlation between images of a scene at different wavelengths. A redundant dictionary of atoms is learned that can sparsely approximate training spectra. The restoration of correct spectra is formulated as an 1 convex optimization problem that minimizes a Mahalanobis-like weighted distance between the restored and corrupt signals as well as the restored signal and the median of the eight connected neighbours of the corrupt signal such that the restored signal is a sparse linear combination of the dictionary atoms. Spectral restoration is followed by spatial restoration using a guided dictionary approach where one dictionary is learned for measured bands and another for a band that is to be spatially restored. By constraining the sparse coding coefficients of both dictionaries to be the same, the restoration of corrupt band is guided by the more reliable measured bands. Experiments on real data and comparison with an existing volumetric image denoising technique shows the superiority of our algorithm.

© 2012 OSA

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(100.4145) Image processing : Motion, hyperspectral image processing

ToC Category:
Image Processing

Original Manuscript: February 23, 2012
Revised Manuscript: April 19, 2012
Manuscript Accepted: April 19, 2012
Published: April 24, 2012

Ajmal Mian and Richard Hartley, "Hyperspectral video restoration using optical flow and sparse coding," Opt. Express 20, 10658-10673 (2012)

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  1. D. Kittle, K. Choi, A. Wagadarikar, and D. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt.49, 6824–6833 (2010). [CrossRef] [PubMed]
  2. M. Shankar, N. Pitsianis, and D. Brady, “Compressive video sensors using multichannel imagers,” Appl. Opt.49, B9–B17 (2010). [CrossRef] [PubMed]
  3. Y. Pati, R. Rexaiifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in Proceedings of the 27th Asilomar Conference on Signals, Systems, and Computers (IEEE, 1993), 40–44. [CrossRef]
  4. R. Tibshirani, “Regression shrinkage and selection via the Lasso,” J. R. Stat. Soc. Ser. B58, 267–288 (1996).
  5. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process.54, 4311–4322 (2006). [CrossRef]
  6. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process.15, 3736–3745 (2006). [CrossRef] [PubMed]
  7. M. Elad and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process.17, 53–69 (2008). [CrossRef] [PubMed]
  8. H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens.44, 397–408 (2006). [CrossRef]
  9. S. Bourguignon, D. Mary, and E. Slezak, “Sparsity-based denoising of hyperspectral astrophysical data with colored noise: Application to the MUSE instrument,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE, 2010), 1–4. [CrossRef]
  10. G. Farnebäck, “Two-frame motion estimation based on polynomial expansion,” in Proceedings of the 13th Scandinavian Conference on Image Analysis (Springer, 2003), 363–370.
  11. J. Parkkinen, J. Hallikainen, and T. Jaaskelainen, “Characteristic spectra of munsell colors,” J. Opt. Soc. Am. A6, 318–322 (1989). [CrossRef]
  12. J. Mairal, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res.11, 19–60 (2010).
  13. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least angle regression,” Ann. Stat.32, 407–499 (2004). [CrossRef]
  14. J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process.19, 2861–2873 (2010). [CrossRef]
  15. P. Ndajah, H. Kikuchi, M. Yukawa, H. Watanabe, and S. Muramatsu, “An investigation on the quality of denoised images,” Int. J. Circuits, Systems and Signal Process.5, 423–434 (2011).

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