Image super-resolution by extreme learning machine

Image super-resolution is the process to generate high-resolution images from low-resolution inputs. In this paper, an efficient image super-resolution approach based on the recent development of extreme learning machine (ELM) is proposed. We aim at reconstructing the high-frequency components containing details and fine structures that are missing from the low-resolution images. In the training step, high-frequency components from the original high-resolution images as the target values and image features from low-resolution images are fed to ELM to learn a model. Given a low-resolution image, the high-frequency components are generated via the learned model and added to the initially interpolated low-resolution image. Experiments show that with simple image features our algorithm performs better in terms of accuracy and efficiency with different magnification factors compared to the state-of-the-art methods.