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Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition
- Source :
- IEEE Access, Vol 6, Pp 1380-1390 (2018)
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
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.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 6
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7188496a5cef4938b57bead5c5376364
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2017.2778947