High-resolution Hyperspectral Imaging via Matrix Factorization

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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) - PowerPoint PPT Presentation

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High-resolution Hyperspectral Imaging via Matrix Factorization

High-resolution Hyperspectral Imaging via Matrix FactorizationRei 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 111My talk is about estimating optical properties of layered surfaces using the spider model.Giga-pixel Camera

M. Ezra et al.Giga-pixel Camera@ Microsoft researchLarge-format lensCCD

2Spectral cameras

LCTF filter35,000 $Hyper-spectral camera55,000 $

Line spectral scanner25,000 $ Expensive Low resolution3Approach

Low-reshyperspectralhigh-resRGBHigh-reshyperspectral imageCombine4Problem formulationW(Image width)H(Image height)SGoal:Given:A1: Limited number of materialsSparse vectorFor all pixel (i,j) Sparse matrixW (Image width)H (Image height)S=00.400.66Sampling of each cameraLow-res cameraRGB cameraSpectrumWavelengthIntensitySparse signal recoveryFilterSignalObservationttmnSparsitySignalBasisWeightsSignalBasisWeights0SS-sparseSparse signal recoveryObservationNeed not to know which bases are importantSparsity and Incoherence mattersA2: Sparsity in high-res imageW (Image width)H (Image height)SSparse coefficientsSparse vectorSimulation experiments

460 nm550 nm620 nm460 nm550 nm620 nm

430 nm490 nm550 nm610 nm670 nm14

Error images of Global PCA with back-projection

Error images of local window with back-projection

Error images of RGB clustering with back-projection

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Estimated430 nm16Groundtruth

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RGBimage18

Errorimage19

HS camera

FilterCMOSLensApertureFocusTranslational stage

Real data experimentInput RGBInput (550nm)Input (620nm)Estimated (550nm)Estimated (620nm)SummaryMethod to reconstruct high-resolution hyperspectral image from Low-res hyperspectral cameraHigh-res RGB camera

Spatial sparsity of hyperspectral inputSearch for a factorization of the input intobasis set of maximally sparse coefcients.24Our approach include three key-points.One is a novel physical model Spider model.One is to estimate the optical properties using spider model.One is simulation of appearances with various degrees of opacity.