Registration Errors: Are They Always Bad for Super-Resolution?
The super-resolution reconstruction (SRR) of images is an ill posed problem. Traditionally, it is treated as a regularized minimization problem. Moreover, one of the major problems concerning SRR is its dependence on an accurate registration. In this paper, we show that a certain amount of registrat...
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Veröffentlicht in: | IEEE transactions on signal processing 2009-10, Vol.57 (10), p.3815-3826 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The super-resolution reconstruction (SRR) of images is an ill posed problem. Traditionally, it is treated as a regularized minimization problem. Moreover, one of the major problems concerning SRR is its dependence on an accurate registration. In this paper, we show that a certain amount of registration error may, in fact, be beneficial for the performance of the least mean square SRR (LMS-SRR) adaptive algorithm. In these cases, the regularization term may be avoided, leading to reduction in computational cost that can be important in real-time SRR applications. |
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ISSN: | 1053-587X 1941-0476 |
DOI: | 10.1109/TSP.2009.2023402 |