A fast single-image super-resolution via directional edge-guided regularized extreme learning regression

Abstract

The goal of super-resolution (SR) is to increase the spatial resolution of a low-resolution (LR) image by a certain factor using either single or multiple LR input images. This paper presents a machine learning-based approach to reconstruct a high-resolution (HR) image from a single LR image. Inspired by the human visual cortex system, which is sensitive to high-frequency (HF) components in an image, we aim to model this concept by training a neural network to estimate the missing HF components that contain structural details. In our method, various directional edge responses at each pixel are considered to obtain more complete HF information and then a regularized extreme learning regression model is trained using a set of LR and HR images. Finally, the trained system is applied to a LR image to generate HR image. The experimental results confirm the effectiveness and efficiency of the proposed scheme in comparison with the state-of-the-art SR methods.