Image Super Resolution Using Multiple Kernel Learning
D.Y.K. Sampath, Sampath Kumar · 2014
Learning-based approaches for image super-resolution (SR) have attracted the attention of researchers in the past few years. We present a novel self-learning approach with multiple kernel learning for adaptive kernel selection for SR. The Multiple Kernel Learning is theoretically and technically very attractive, because it learns the kernel weights and the classifier simultaneously based on the margin criterion. With theoretical supports of kernel matching search method and Optimization approach (Gradient) are proposed in SR framework learns and selects the optimal Kernel ridge regression model when producing an SR image, which results in the minimum SR reconstruction error. Evaluate this method on a variety of images, and obtain very promising SR results. In most cases, this method quantitatively and qualitatively outperforms bi-cubic interpolation and state-of-the-art learning based SR approaches.