image superresolution based on locally adaptive mixed-norm
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2010
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Abstract
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.
| Reference Key |
omer2010journalimage
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|---|---|
| Authors | ;Osama A. Omer;Toshihisa Tanaka |
| Journal | Molecular diversity |
| Year | 2010 |
| DOI |
10.1155/2010/435194
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| URL | |
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