| Paper: | IMDSP-P9.1 | ||
| Session: | Filtering, Interpolation and Superresolution | ||
| Time: | Wednesday, May 17, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Image and Multidimensional Signal Processing: Interpolation and Super-resolution | ||
| Title: | Single Image Superresolution Based on Support Vector Regression | ||
| Authors: | Karl Ni, Sanjeev Kumar, Nuno Vasconcelos, Truong Q. Nguyen, University of California, San Diego, United States | ||
| Abstract: | Support vector machine (SVM) regression is considered for a statistical method of single frame superresolution in both the spatial and Discrete Cosine Transform (DCT) domains. As opposed to current classification techniques, regression allows considerably more freedom in the determination of missing high-resolution information. In addition, because SVM regression approaches the superresolution problem as an estimation problem with a criterion of image correctness rather than visual acceptableness, its optimization results in better mean-squared error results. With the addition of structure in the DCT domain, DCT domain image superresolution is further improved. | ||
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