Hyperspectral Denoising via Global Variation and Local Structure Low-Rank Model
Lan Li, Meiping Song, Qiang Zhang, Yushuai Dong
Abstract
Hyperspectral images (HSIs) are often disturbed by various kinds of noises. This paper proposes a global variation and local structure low-rank model (GLLR) for HSI denoising by integrating spatial segmentation smoothing and spectral low-rank properties. Compared with existing denoising methods, the proposed method considers not only the global low-rank property but also the local structure low-rank property of HSIs. Specifically, the GLLR describes the global correlation and segmental smoothing structure of the HSI by the correlated total variation. In addition, we construct a new structural low-rank prior, called the local minimum difference (LMD) low-rank. With LMD low-rank property of HSI, GLLR can remove noise while retaining useful structural information in the HSI. Then, an ALM-based optimization algorithm is devised to solve the objective functions for the presented model. Finally, comparison experiments with existing methods are conducted on synthetic and real datasets to demonstrate the effectiveness and superiority of the proposed method.