Enhanced joint estimation-based hyperspectral image super resolution

Abstract

A new hyperspectral image super-resolution method from a Low-resolution(LR) image and an HR reference image of the same scene is proposed. The estimation of the HR Hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse course based on the prior knowledge of the spatial–spectral scarcity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene in first learned from the input LR image. Specifically, an efficient nonnegative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of nonnegative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on public datasets suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency.