A Hyperspectral Image Denoising Approach via Low-Rank Matrix Recovery and Greedy Bilateral

Abstract

The hyperspectral image (HSI) can provide useful information about the desired objects using spectral, spatial, and band channels. However, the image quality is typically distorted due to the limitations of sensing conditions and hardware operations. Consequently, the HSI is typically contaminated by a mixture noise during the acquisition process, including dead lines, stripes, Gaussian noise and impulse noise. In this paper, we introduce a new denoising model based on low-rank matrix recovery (LRMR), which can effectively remove various kinds of noise in HSI data. The low-rank property of the hyperspectral imagery is exploited by converting a patch of the HSI data from 3-D matrix into a 2-D matrix. The dead lines, stripes, and impulse noise are all modelled as sparse noise. To efficiently remove mixed noise and enhance performance, we develop an iterative algorithm using greedy bilateral technique to solve the optimization problem. To illustrate the proposed method’s efficacy in restoring HSI, both simulated and real-world HSI experiments are conducted.