3D imaging spectroscopy for measuring hyperspectral patterns on solid objects
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Dictionary Learning for Noisy and Incomplete Hyperspectral Images
SIAM Journal on Imaging Sciences
Adaptive Compressed Image Sensing Using Dictionaries
SIAM Journal on Imaging Sciences
Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives
Image and Vision Computing
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Hyperspectral imaging is a promising tool for applications in geosensing, cultural heritage and beyond. However, compared to current RGB cameras, existing hyperspectral cameras are severely limited in spatial resolution. In this paper, we introduce a simple new technique for reconstructing a very high-resolution hyperspectral image from two readily obtained measurements: A lower-resolution hyper-spectral image and a high-resolution RGB image. Our approach is divided into two stages: We first apply an unmixing algorithm to the hyperspectral input, to estimate a basis representing reflectance spectra. We then use this representation in conjunction with the RGB input to produce the desired result. Our approach to unmixing is motivated by the spatial sparsity of the hyperspectral input, and casts the unmixing problem as the search for a factorization of the input into a basis and a set of maximally sparse coefficients. Experiments show that this simple approach performs reasonably well on both simulations and real data examples.