Improved image recovery from compressed data contaminated with impulsive noise
Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in...
| Main Authors: | , |
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| Format: | Journal Article |
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IEEE Signal Processing Society
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/30022 |
| _version_ | 1848752968256454656 |
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| author | Pham, DucSon Venkatesh, Svetha |
| author_facet | Pham, DucSon Venkatesh, Svetha |
| author_sort | Pham, DucSon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the /2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies. |
| first_indexed | 2025-11-14T08:17:03Z |
| format | Journal Article |
| id | curtin-20.500.11937-30022 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:17:03Z |
| publishDate | 2011 |
| publisher | IEEE Signal Processing Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-300222017-09-13T16:07:34Z Improved image recovery from compressed data contaminated with impulsive noise Pham, DucSon Venkatesh, Svetha inverse problems impulsive noise robust statistics Compressed sensing (CS) image compression robust recovery Compressed sensing (CS) is a new information sampling theory for acquiring sparse or compressible data with much fewer measurements than those otherwise required by the Nyquist/Shannon counterpart. This is particularly important for some imaging applications such as magnetic resonance imaging or in astronomy. However, in the existing CS formulation, the use of the /2 norm on the residuals is not particularly efficient when the noise is impulsive. This could lead to an increase in the upper bound of the recovery error. To address this problem, we consider a robust formulation for CS to suppress outliers in the residuals. We propose an iterative algorithm for solving the robust CS problem that exploits the power of existing CS solvers. We also show that the upper bound on the recovery error in the case of non-Gaussian noise is reduced and then demonstrate the efficacy of the method through numerical studies. 2011 Journal Article http://hdl.handle.net/20.500.11937/30022 10.1109/TIP.2011.2162418 IEEE Signal Processing Society restricted |
| spellingShingle | inverse problems impulsive noise robust statistics Compressed sensing (CS) image compression robust recovery Pham, DucSon Venkatesh, Svetha Improved image recovery from compressed data contaminated with impulsive noise |
| title | Improved image recovery from compressed data contaminated with impulsive noise |
| title_full | Improved image recovery from compressed data contaminated with impulsive noise |
| title_fullStr | Improved image recovery from compressed data contaminated with impulsive noise |
| title_full_unstemmed | Improved image recovery from compressed data contaminated with impulsive noise |
| title_short | Improved image recovery from compressed data contaminated with impulsive noise |
| title_sort | improved image recovery from compressed data contaminated with impulsive noise |
| topic | inverse problems impulsive noise robust statistics Compressed sensing (CS) image compression robust recovery |
| url | http://hdl.handle.net/20.500.11937/30022 |