Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal

Abstract

Since the facility restrictions and weather conditions, hyperspectral image (HSI) is generally seriously polluted by a variety of noises. Recently, the method based on block term decomposition with rank-(L, L, 1) (BTD) has attracted wide attention in HSI mixed noise removal. BTD factorizes thirdorder HSI data into the sum of a series of component tensors, where each of the component tensors is represented by the outer product of a rank-L matrix ArB T r and a column vector cr. BTD has clear physical interpretation because its latent factors ArB T r and cr can be interpreted abundance map and spectral signature respectively. The essential uniqueness of BTD is under the lowrank assumption of ArB T r . However, the low-rank assumption is not always held in real scenarios. The BTD-based method usually sets L to full rank to achieve satisfactory results. In this paper, we suggest a novel model based on nonlocal block term decomposition (NLBTD) for HSI mixed noise removal. More specifically, for each grouped similar image block, BTD is introduced to capture nonlocal self-similarity and global spectral lowrankness, the unidirectional total variation (TV) is introduced to preserve local spectral smoothness. By faithfully exploring nonlocal self-similarity, global spectral low-rankness, and local spectral smoothness, the proposed method is expected to produce promising results with guarantee the essential uniqueness of BTD. To tackle the resulting model, we design an efficient algorithm based on the proximal alternating minimization with the theoretical guarantees. Extensive numerical experiments in HSI mixed noise removal demonstrate that the proposed NLBTD method achieves satisfactory performance compared with stateof-the-art methods.

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

“…An algorithm called the alternating direction multiplier model is used to enhance the denoising ability of the DIP model. Zeng et al [35] removed the HSI mixed noise by proposing nonlocal block-term decomposition (NLBTD). Global spectral and non-local self-similarity features are captured by using BTD for preserving the smoothness of local spectra.…”

Piecewise Weighted Smoothing Regularization in Tight Framelet Domain for Hyperspectral Image Restoration

“…It is widely used in environmental monitoring, material classification [1], target detection [2] and others [3]. However, in the course of data acquisition and processing, hyperspectral images (HSIs) are easily contaminated by different noise, such as Gaussian noise, salt and pepper noise and stripe noise [4]. This will seriously affect the quality and application of hyperspectral data [5]- [7].…”