Image Superresolution Based on Locally Adaptive Mixed-Norm
In a typical superresolution algorithm, fusion error modeling, including registration error and additive noise, has a great influence on the performance of the super-resolution algorithms. In this letter, we show that the quality of the reconstructed high-resolution image can be increased by exploit...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2010/435194 |
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| _version_ | 1850109608622817280 |
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| author | Osama A. Omer Toshihisa Tanaka |
| author_facet | Osama A. Omer Toshihisa Tanaka |
| author_sort | Osama A. Omer |
| collection | DOAJ |
| description | In a typical superresolution algorithm, fusion error modeling, including registration error and additive noise, has a great influence on the performance of the super-resolution algorithms. In this letter, we show that the quality of the reconstructed high-resolution image can be increased by exploiting proper model for the fusion error. To properly model the fusion error, we propose to minimize a cost function that consists of locally and adaptively weighted L1- and L2-norms considering the error model. Binary weights are used so as to adaptively select L1- or L2-norm, based on the local errors. Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either L1- or L2-norm. |
| format | Article |
| id | doaj-art-4ae785cc2e55458e9e045a2f29776b06 |
| institution | OA Journals |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-4ae785cc2e55458e9e045a2f29776b062025-08-20T02:38:02ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552010-01-01201010.1155/2010/435194435194Image Superresolution Based on Locally Adaptive Mixed-NormOsama A. Omer0Toshihisa Tanaka1Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, JapanDepartment of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, JapanIn a typical superresolution algorithm, fusion error modeling, including registration error and additive noise, has a great influence on the performance of the super-resolution algorithms. In this letter, we show that the quality of the reconstructed high-resolution image can be increased by exploiting proper model for the fusion error. To properly model the fusion error, we propose to minimize a cost function that consists of locally and adaptively weighted L1- and L2-norms considering the error model. Binary weights are used so as to adaptively select L1- or L2-norm, based on the local errors. Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either L1- or L2-norm.http://dx.doi.org/10.1155/2010/435194 |
| spellingShingle | Osama A. Omer Toshihisa Tanaka Image Superresolution Based on Locally Adaptive Mixed-Norm Journal of Electrical and Computer Engineering |
| title | Image Superresolution Based on Locally Adaptive Mixed-Norm |
| title_full | Image Superresolution Based on Locally Adaptive Mixed-Norm |
| title_fullStr | Image Superresolution Based on Locally Adaptive Mixed-Norm |
| title_full_unstemmed | Image Superresolution Based on Locally Adaptive Mixed-Norm |
| title_short | Image Superresolution Based on Locally Adaptive Mixed-Norm |
| title_sort | image superresolution based on locally adaptive mixed norm |
| url | http://dx.doi.org/10.1155/2010/435194 |
| work_keys_str_mv | AT osamaaomer imagesuperresolutionbasedonlocallyadaptivemixednorm AT toshihisatanaka imagesuperresolutionbasedonlocallyadaptivemixednorm |