Single-Image Super-Resolution Using Multihypothesis Prediction

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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
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
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