Abstract
This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust $\ell _1$ data fidelity instead of using the $\ell _2$ data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal.
Highlights
H YPERSPECTRAL imaging camera measures electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with high spectral resolution
Poissonian noise is becoming the main concern in real hyperspectral imaging [2] as spectral resolution of imagers increases in the new generation hyperspectral sensing systems
The results demonstrate it is possible to sidestep the model of mixed noise
Summary
H YPERSPECTRAL imaging camera measures electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with high spectral resolution. The increase of spectral resolution often implies an increase in the noise corrupted in the image formation process. This degradation mechanism limits the quality of extracted information and its potential applications. Poissonian noise is becoming the main concern in real hyperspectral imaging [2] as spectral resolution of imagers increases in the new generation hyperspectral sensing systems. The spectral bandwidth decreases implying that, everything else kept constant, each spectral channel receives less photons, yielding higher levels of Poissonian noise. An alternative way is to convert Poissonian noise into approximate additive
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