Single Image Superresolution Based on Support Vector Regression
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, since SVM regression approaches the superresolution problem as an estimation problem with a criterion of image correctness rather than visual acceptableness, its optimization results have better mean-squared error. With the addition of structure in the DCT coefficients, DCT domain image superresolution is further improved