Fast and robust multiframe super resolution.
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.
Duke Scholars
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Related Subject Headings
- Subtraction Technique
- Signal Processing, Computer-Assisted
- Sensitivity and Specificity
- Reproducibility of Results
- Pattern Recognition, Automated
- Numerical Analysis, Computer-Assisted
- Information Storage and Retrieval
- Image Interpretation, Computer-Assisted
- Image Enhancement
- Computer Simulation
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Subtraction Technique
- Signal Processing, Computer-Assisted
- Sensitivity and Specificity
- Reproducibility of Results
- Pattern Recognition, Automated
- Numerical Analysis, Computer-Assisted
- Information Storage and Retrieval
- Image Interpretation, Computer-Assisted
- Image Enhancement
- Computer Simulation