Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
This work addresses efficient restoration of hyperspectral images, which is important for remote sensing and imaging applications, but it appears incremental as it builds on existing low-rank and sparse representation techniques.
The paper tackled hyperspectral image restoration by introducing FastHyDe for denoising and FastHyIn for inpainting, which exploit low-rank and sparse representations to achieve competitive results with state-of-the-art methods while significantly reducing computational complexity.
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.