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An image super-resolution algorithm for different error levels per frame
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AI
DOI：10.1109/TIP.2005.860599.png)
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Abstract
En 中文
In this paper, we propose an image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. These inaccurate estimates, along with the additive Gaussian noise in the low-resolution (LR) image sequence, result in different noise level for each frame. In the proposed algorithm, the LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated. A translational motion model is assumed. The convergence property of the proposed algorithm is analyzed in detail. Our experimental results using both real and synthetic data show the effectiveness of the proposed algorithm.
Keywords:
regularization
resolution enhancement
superresolution
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
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Cited Papers
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Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time
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