dynamic range

image fusion

quality assessment

tone mapping

hyperspectral image

hyperspectral data

satellite images

hyperspectral anomaly

hsi classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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.

Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations