  This paper introduces two very fast and competitive hyperspectral image (HSI)
restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising
algorithm able to cope with Gaussian and Poissonian noise, and fast
hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs
where some observations from known pixels in some known bands are missing.
FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI
representations linked with their low-rank and self-similarity characteristics.
In a series of experiments with simulated and real data, the newly introduced
FastHyDe and FastHyIn compete with the state-of-the-art methods, with much
lower computational complexity.
