Abstract

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 short-comings. We propose an alternate approach using L1 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

Computer scienceArtificial intelligenceRegularization (linguistics)Computer visionRobustness (evolution)Image resolutionSuperresolutionImage restorationMinificationIterative reconstructionResolution (logic)Noise (video)AlgorithmImage processingImage (mathematics)

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Publication Info

Year
2004
Type
article
Volume
13
Issue
10
Pages
1327-1344
Citations
1998
Access
Closed

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1998
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Cite This

Sina Farsiu, Michael D. Robinson, Michael Elad et al. (2004). Fast and Robust Multiframe Super Resolution. IEEE Transactions on Image Processing , 13 (10) , 1327-1344. https://doi.org/10.1109/tip.2004.834669

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DOI
10.1109/tip.2004.834669