dc.contributor |
Háskóli Íslands |
dc.contributor |
University of Iceland |
dc.contributor.author |
Huang, Zhihong |
dc.contributor.author |
Li, Shutao |
dc.contributor.author |
Fang, Leyuan |
dc.contributor.author |
Li, Huali |
dc.contributor.author |
Benediktsson, Jon Atli |
dc.date.accessioned |
2018-09-03T15:01:53Z |
dc.date.available |
2018-09-03T15:01:53Z |
dc.date.issued |
2018 |
dc.identifier.citation |
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 |
dc.identifier.issn |
2169-3536 |
dc.identifier.uri |
https://hdl.handle.net/20.500.11815/821 |
dc.description.abstract |
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. |
dc.description.sponsorship |
This paper was supported in part by the National Natural Science Foundation of China under Grant 61301255, Grant 61771192, and
Grant 61471167, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part
by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the
Science and Technology Plan Project Fund of Hunan Province under Grant 2015WK3001 and Grant 2017RS3024. |
dc.format.extent |
1380-1390 |
dc.language.iso |
en |
dc.publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
dc.relation.ispartofseries |
IEEE Access;6 |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Hyperspectral image |
dc.subject |
Denoising |
dc.subject |
Sparse and low-rank tensor decomposition |
dc.subject |
Nonlocal similarity |
dc.subject |
Myndvinnsla |
dc.title |
Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition |
dc.type |
info:eu-repo/semantics/article |
dc.description.version |
Peer Reviewed |
dc.identifier.journal |
IEEE Access |
dc.identifier.doi |
10.1109/ACCESS.2017.2778947 |
dc.relation.url |
http://xplorestaging.ieee.org/ielx7/6287639/8274985/08125087.pdf?arnumber=8125087 |
dc.contributor.department |
Rafmagns- og tölvuverkfræðideild (HÍ) |
dc.contributor.department |
Faculty of Electrical and Computer Engineering (UI) |
dc.contributor.school |
Verkfræði- og náttúruvísindasvið (HÍ) |
dc.contributor.school |
School of Engineering and Natural Sciences (UI) |