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Fast and robust multiframe super resolution
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DOI：10.1109/TIP.2004.834669.png)
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Abstract
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Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using L-1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.
Keywords:
bilateral filter
deblurring
enhancement
image restoration
multiframe
regularization
robust estimation
super resolution
total variation (TV)
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