Analysis SimCO Algorithms for Sparse Analysis Model Based Dictionary Learning
- Submitting institution
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The University of Surrey
- Unit of assessment
- 12 - Engineering
- Output identifier
- 9010194_1
- Type
- D - Journal article
- DOI
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10.1109/TSP.2015.2483480
- Title of journal
- IEEE Transactions on Signal Processing
- Article number
- -
- First page
- 417-431
- Volume
- 64
- Issue
- 2
- ISSN
- 1941-0476
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The is the first work that presents the idea of sub-space decomposition for sparse analysis model based dictionary learning. A new theoretical signal representation framework is introduced that can be applied to analysis of diverse data (sound, image, video, biomedical). Research was funded by the flagship Dstl University Defence Research Collaboration, and the software published as MATLAB Sparsity Toolbox: https://udrc.eng.ed.ac.uk/data-centre. The work was presented as a Keynote Speech at international conference FSDM 2016, Macau, China. The work led to MoD-MarCE project with Atlas “Exploiting sparsity for submarine hull mounted arrays” and was used to solve the SAR image denoising problem(https://doi.org/10.1016/j.sigpro.2017.01.032)
- Author contribution statement
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- Non-English
- No
- English abstract
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