Adaptive Hyperspectral Mixed Noise Removal
Tai-Xiang Jiang, Lina Zhuang, Ting‐Zhu Huang, Xi-Le Zhao, José M. Bioucas‐Dias
Abstract
This article proposes a new denoising method for hyperspectral images (HSIs) corrupted by mixtures (in a statistical sense) of stripe noise, Gaussian noise, and impulsive noise. The proposed method has three distinctive features: 1) it exploits the intrinsic characteristics of HSIs, namely, low-rank and self-similarity; 2) the observation noise is assumed to be additive and modeled by a mixture of Gaussian (MoG) densities; 3) the inference is performed with an expectation maximization (EM) algorithm, which, in addition to the clean HSI, also estimates the mixture parameters (posterior probability of each mode and variances). Comparisons of the proposed method with state-of-the-art algorithms provide experimental evidence of the effectiveness of the proposed denoising algorithm. A MATLAB demo of this work will be available at <uri>https://github.com/TaiXiangJiang</uri> for the sake of reproducibility.