A guidable nonlocal low-rank approximation model for hyperspectral image denoising

Abstract

Hyperspectral image (HSI) denoising is an essential preprocessing step for improving HSI applications. Recently, subspace-based nonlocal low-rank approximation (SNLR) methods have shown their superiority. However, most of these methods ignore such a potentially important phenomenon, that is, real HSIs contain many high signal-to-noise ratio bands (HSNRBs), and thus result in the underutilization of information. In this paper, we propose a new method called Subspace-based Guided Nonlocal Low-Rank Approximation (SGNLR) for HSI denoising. Our method takes advantage of the abundant spatial information in the representation coefficients of HSNRBs to improve denoising performances. Specifically, we employ low-rank subspace representation to exploit the global spectral correlation of HSI and transform the HSI denoising task as the estimation of spectral basis and spatial representation coefficients (SRCs). Motivated by the consistency of coefficient features between the whole HSI and HSNRBs, we employ the SRCs of HSNRBs to guide the restoration of target coefficients. To restore the SRCs accurately, we design a powerful nonlocal low-rank approximation that takes into account the nonlocal self-similarity (NSS) of SRCs. An efficient algorithm based on alternating minimization is developed to optimize the proposed model. Extensive experiments on both simulated and real-world data demonstrate the outperformance of our method.