Unmixing-based denoising for destriping and inpainting of hyperspectral images

Unmixing-based Denoising exploits spectral unmixing results to selectively recover bands affected by a low Signal-to-Noise Ratio in hypespectral images. This paper proposes to apply this algorithm, which operates pixelwise, for the inpainting of corrupted pixels and the removal of drop-out artifacts in hy-perspectral scenes. The reported experiments are characterized by a low reconstruction error for the reconstructed spectra and a high visual quality of the processed images, and outperform state of the art methods in terms of reconstruction error.