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Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition

Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition


Titill: Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition
Höfundur: Huang, Zhihong
Li, Shutao
Fang, Leyuan
Li, Huali
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Útgáfa: 2018
Tungumál: Enska
Umfang: 1380-1390
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
Birtist í: IEEE Access;6
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2017.2778947
Efnisorð: Hyperspectral image; Denoising; Sparse and low-rank tensor decomposition; Nonlocal similarity; Myndvinnsla
URI: https://hdl.handle.net/20.500.11815/821

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Tilvitnun:

Huang, Z., Li, S., Fang, L., Li, H., & Benediktsson, J. A. (2018). Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition. IEEE Access, 6, 1380-1390. doi:10.1109/ACCESS.2017.2778947

Útdráttur:

Hyperspectral image (HSI) is usually corrupted by various types of noise, including Gaussian noise, impulse noise, stripes, deadlines, and so on. Recently, sparse and low-rank matrix decomposition (SLRMD) has demonstrated to be an effective tool in HSI denoising. However, the matrix-based SLRMD technique cannot fully take the advantage of spatial and spectral information in a 3-D HSI data. In this paper, a novel group sparse and low-rank tensor decomposition (GSLRTD) method is proposed to remove different kinds of noise in HSI, while still well preserving spectral and spatial characteristics. Since a clean 3-D HSI data can be regarded as a 3-D tensor, the proposed GSLRTD method formulates a HSI recovery problem into a sparse and low-rank tensor decomposition framework. Specifically, the HSI is first divided into a set of overlapping 3-D tensor cubes, which are then clustered into groups by K-means algorithm. Then, each group contains similar tensor cubes, which can be constructed as a new tensor by unfolding these similar tensors into a set of matrices and stacking them. Finally, the SLRTD model is introduced to generate noisefree estimation for each group tensor. By aggregating all reconstructed group tensors, we can reconstruct a denoised HSI. Experiments on both simulated and real HSI data sets demonstrate the effectiveness of the proposed method.

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