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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. Spectrum

  4. RGB vs. Spectrum

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

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

  7. Representation: Basis function H (Image height) + + + = x 0 x 1.0 x 0 x 0 S W (Image width) 0 1.0 0 … 0 … =

  8. Two-step approach • Estimate basis functions from hyperspectral image • For each pixel in high-res RGB image, estimate coefficients for the basis functions

  9. 1: Limited number of materials • At each pixel of , only a few () materials are present 0 0.4 0 … 0.6 … H (Image height) = S W (Image width) Sparsevector For all pixel (i,j) Sparse matrix

  10. 2: Sparsity in high-res image H S W Sparse coefficients Reconstruction

  11. Simulation experiments

  12. 460 nm 550 nm 620 nm 460 nm 550 nm 620 nm

  13. 430 nm 490 nm 550 nm 610 nm 670 nm

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

  15. Estimated 430 nm

  16. Ground truth

  17. RGB image

  18. Error image

  19. HS camera Lens CMOS Aperture Focus Filter Translational stage

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

  21. 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 functions • set of maximally sparse coefficients

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