Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property
Abstract During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For decades, numerous HSI denoising methods have been proposed. However, these methods rarely consider the stripe noise as an independent componen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-10-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-022-00901-3 |
| _version_ | 1811238912023068672 |
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| author | Zhi Zhang Fang Yang |
| author_facet | Zhi Zhang Fang Yang |
| author_sort | Zhi Zhang |
| collection | DOAJ |
| description | Abstract During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For decades, numerous HSI denoising methods have been proposed. However, these methods rarely consider the stripe noise as an independent component, thus cannot effectively remove the stripe noise. In this paper, we propose a mixed noise removal algorithm to destripe an HSI by taking advantage of the low-rank property of stripe noise. In the meantime, sparse representation and graph Laplacian regularization are utilized to remove Gaussian and sparse noise. Roughly speaking, the sparse representation helps achieve the approximation of the original image. A graph Laplacian regularization term can ensure the non-local spatial similarity of an HSI. Separate constraints on the sparse coefficient matrix and stripe noise components can help remove different types of noises. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method for HSI restoration. |
| first_indexed | 2024-04-12T12:50:36Z |
| format | Article |
| id | doaj.art-686d997b02634ed6942b6342a5886871 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-12T12:50:36Z |
| publishDate | 2022-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-686d997b02634ed6942b6342a58868712022-12-22T03:32:30ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802022-10-012022111810.1186/s13634-022-00901-3Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank propertyZhi Zhang0Fang Yang1Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and TechnologyEngineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and TechnologyAbstract During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For decades, numerous HSI denoising methods have been proposed. However, these methods rarely consider the stripe noise as an independent component, thus cannot effectively remove the stripe noise. In this paper, we propose a mixed noise removal algorithm to destripe an HSI by taking advantage of the low-rank property of stripe noise. In the meantime, sparse representation and graph Laplacian regularization are utilized to remove Gaussian and sparse noise. Roughly speaking, the sparse representation helps achieve the approximation of the original image. A graph Laplacian regularization term can ensure the non-local spatial similarity of an HSI. Separate constraints on the sparse coefficient matrix and stripe noise components can help remove different types of noises. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed method for HSI restoration.https://doi.org/10.1186/s13634-022-00901-3Hyperspectral image (HSI)Sparse representationGraph Laplacian regularizationDenoiseDestripe |
| spellingShingle | Zhi Zhang Fang Yang Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property EURASIP Journal on Advances in Signal Processing Hyperspectral image (HSI) Sparse representation Graph Laplacian regularization Denoise Destripe |
| title | Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property |
| title_full | Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property |
| title_fullStr | Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property |
| title_full_unstemmed | Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property |
| title_short | Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property |
| title_sort | hyperspectral image denoising and destriping based on sparse representation graph laplacian regularization and stripe low rank property |
| topic | Hyperspectral image (HSI) Sparse representation Graph Laplacian regularization Denoise Destripe |
| url | https://doi.org/10.1186/s13634-022-00901-3 |
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