A Low-Rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising

As a 3-order tensor, a hyperspectral image (HSI) has dozens of spectral bands, which can deliver more information of real scenes. However, real HSIs are often corrupted by noises in the sensing process, which deteriorates the performance of higher-level detection tasks. In this paper, we propose a L...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2020, Vol.68, p.1168-1180
Main Authors: Gong, Xiao, Chen, Wei, Chen, Jie
Format: Article
Language:English
Subjects:
Approximation
Data models
denoising
Dictionaries
dictionary learning
Hyperspectral imaging
Image denoising
Learning
Low-rank tensor
Machine learning
Mathematical analysis
Noise reduction
Representations
Spectral analysis
Spectral bands
Tensors
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Summary:As a 3-order tensor, a hyperspectral image (HSI) has dozens of spectral bands, which can deliver more information of real scenes. However, real HSIs are often corrupted by noises in the sensing process, which deteriorates the performance of higher-level detection tasks. In this paper, we propose a Low-rank Tensor Dictionary Learning (LTDL) method for HSI denoising. Differing to existing low-rank based methods, we consider a nearly low-rank approximation, which is closer to the latent low-rank structure of the clean groups of real HSIs. Furthermore, the proposed method benefits from multitask learning gain by learning a spatial dictionary and a spectral dictionary shared among different tensor groups. While most existing work usually consider sparse representations, we exploit a simultaneously sparse and low-rank tensor representation model to enhance the capability of dictionary learning, which is inspired from the observation of the low rank structure in HSI tensor groups. Experiments on synthetic data validate the effectiveness of dictionary learning by the LTDL. Experiments on real HSIs demonstrate the superior denoising performance of the proposed method both visually and quantitatively as compared with state-of-the-art methods along this line of research.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.2971441