Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

Hemant Kumar Aggarwal; Angshul Majumdar

doi:10.1117/1.JEI.24.1.013027

17 February 2015 Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

Hemant Kumar Aggarwal, Angshul Majumdar

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 1, 013027 (February 2015). https://doi.org/10.1117/1.JEI.24.1.013027

Abstract

This paper proposes a technique for reducing impulse noise from corrupted hyperspectral images. We exploit the spatiospectral correlation present in hyperspectral images to sparsify the datacube. Since impulse noise is sparse, denoising is framed as an ^ℓ₁-norm regularized^ℓ₁-norm data fidelity minimization problem. We derive an efficient split Bregman-based algorithm to solve the same. Experiments on real datasets show that our proposed technique, when compared with state-of-the-art denoising algorithms, yields better results.

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Hemant Kumar Aggarwal and Angshul Majumdar "Exploiting spatiospectral correlation for impulse denoising in hyperspectral images," Journal of Electronic Imaging 24(1), 013027 (17 February 2015). https://doi.org/10.1117/1.JEI.24.1.013027

Published: 17 February 2015

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