Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.


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 hyperspectral image and a high-resolution RGB image. Our approach is divided into two stages: We ?rst apply an unmixing algorithm to the hyperspectral input, to estimate a basis representing re?ectance 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 coef?cients. Experiments show that this simple approach performs reasonably well on both simulations and real data examples.

Questions and Answers

You need to be logged in to be able to post here.