Hyperspectral Mixed Noise Removal By <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-Norm-Based Subspace Representation

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 ℓ1 data fidelity instead of using the ℓ1 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.

Keywords

• High-dimensional data
• hyperspectral destriping
• hyperspectral restoration
• low-rank representation
• nonlocal patch
• self-similarity