Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance…

• NN
  National Natural Science Foundation of China

  Awards: 61472301, 61372131, 61227004, 61390512, 61471281
• SR
  Specialized Research Fund for the Doctoral Program of Higher Education of China

  Award: 20130203130001