Special Issue Article Open Access

A Self-Learning Optimization Approach to Single Image Super-Resolution using Kernel ridge regression model

Abstract

Learning-based approaches for image super-resolution (SR) have attracted the attention from 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 our SR framework learns and selects the optimal Kernel ridge regression model when producing an SR image, which results in the minimum SR reconstruction error. We evaluate our method on a variety of images, and obtain very promising SR results. In most cases, our method quantitatively and qualitatively outperforms bi-cubic interpolation and state-of-the-art learning based SR approaches.

Ms. Dharani S

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