Single-Image Super-Resolution Using Multihypothesis Prediction
By: Researcher II
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| Authors | C. Chen, and J. E. Fowler |
| Journal/Conference Name | The 46th Asilomar Conference on Signals, Systems, and Computers |
| Paper Category | ECE |
| Paper Abstract | Single-image super-resolution driven by multihypothesis prediction is considered. The proposed strategy exploits self-similarities existing between image patches within a single image. Specifically, each patch of a low-resolution image is represented as a linear combination of spatially surrounding hypothesis patches. The coefficients of this representation are calculated using Tikhonov regularization and then used to generate a high-resolution image. Experimental results reveal that the proposed algorithm offers significantly higher-quality super-resolution than bicubic interpolation without the cost of training on an extensive training set of imagery as is typical of competing single-image techniques. |
| Date of publication | 2012 |
| Code Programming Language | MATLAB |
| Comment |
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