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High-resolution Hyperspectral Imaging via Matrix Factorization

High-resolution Hyperspectral Imaging via Matrix Factorization. Rei Kawakami 1 John Wright 2 Yu-Wing Tai 3 Yasuyuki Matsushita 2 Moshe Ben-Ezra 2 Katsushi Ikeuchi 3 1 University of Tokyo, 2 Microsoft Research Asia (MSRA), 3 Korea Advanced Institute of Science and Technology (KAIST)

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High-resolution Hyperspectral Imaging via Matrix Factorization

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  1. High-resolution Hyperspectral Imaging via Matrix Factorization Rei Kawakami1 John Wright2 Yu-Wing Tai3 Yasuyuki Matsushita2 Moshe Ben-Ezra2 Katsushi Ikeuchi3 1University of Tokyo, 2Microsoft Research Asia (MSRA), 3Korea Advanced Institute of Science and Technology (KAIST) CVPR 11

  2. Giga-pixel Camera Large-format lens CCD Giga-pixel Camera @ Microsoft research M. Ezra et al.

  3. Spectral cameras LCTF filter 35,000 $ Hyper-spectral camera 55,000 $ Line spectral scanner 25,000 $ • Expensive • Low resolution

  4. Approach Combine high-res RGB Low-res hyperspectral High-res hyperspectral image

  5. Problem formulation Goal: H (Image height) S W (Image width) Given:

  6. A1: Limited number of materials 0 0.4 0 … 0.6 … H (Image height) = S W (Image width) Sparsevector For all pixel (i,j) Sparse matrix

  7. Sampling of each camera • Low-res camera • RGBcamera Intensity Spectrum Wavelength

  8. Sparse signal recovery Observation Filter Signal t t n m

  9. Sparsity Signal Basis Weights Signal Basis Weights S 0 S-sparse

  10. Sparse signal recovery Observation Need not to know which bases are important Sparsity and Incoherence matters

  11. A2: Sparsity in high-res image H (Image height) S W (Image width) Sparse vector Sparse coefficients

  12. Simulation experiments

  13. 460 nm 550 nm 620 nm 460 nm 550 nm 620 nm

  14. 430 nm 490 nm 550 nm 610 nm 670 nm

  15. Error images of Global PCA with back-projection Error images of local window with back-projection Error images of RGB clustering with back-projection

  16. Estimated 430 nm

  17. Ground truth

  18. RGB image

  19. Error image

  20. HS camera Aperture CMOS Lens Focus Filter Translational stage

  21. Real data experiment Input RGB Input (550nm) Estimated (550nm) Input (620nm) Estimated (620nm)

  22. Summary • Method to reconstruct high-resolution hyperspectral image from • Low-res hyperspectral camera • High-res RGB camera • Spatial sparsity of hyperspectral input • Search for a factorization of the input into • basis • set of maximally sparse coefficients.

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