The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration
Abstract
In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regularization, and L1-norm together in a unified framework. The nuclear norm is used to exploit the spectral low-rank property, and the TV regularization is adopted to explore the spatial piecewise smooth structure of the HSI. At the same time, the sparse noise, which includes stripes, impulse noise, and dead pixels, is detected by the L1-norm regularization. To tradeoff the nuclear norm and TV regularization and to further remove the Gaussian noise of the HSI, we also restrict the rank of the clean image to be no larger than the number of endmembers. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed LRTV method for HSI restoration.
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Cites
Image quality assessment: from error visibility to structural similarity
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Nonlinear total variation based noise removal algorithms
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Robust principal component analysis?
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A Singular Value Thresholding Algorithm for Matrix Completion
Cai, J.-F., Candès, E. J., & Shen, Z. (2010) · SIAM Journal on Optimization · doi:10.1137/080738970 · Cited by 5,809
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
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Constrained Optimization and Lagrange Multiplier Methods
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Probabilistic Matrix Factorization
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Robust Recovery of Subspace Structures by Low-Rank Representation
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Bioucas-Dias, J. M., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., & Chanussot, J. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2194696 · Cited by 2,836
Cited by
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
Zhuang, L., & Bioucas-Dias, J. M. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2796570 · Cited by 363
Metadata
- Authors
- He, W., Zhang, H., Zhang, L., & Shen, H.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2016-01
- Volume
- 54
- Issue
- 1
- Pages
- 178-188
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W1944540851
- Cited by
- 667
- References
- 42
- FWCI
- 32.93
- Type
- journal article
- Language
- en
- Open access
- closed
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