Fast single image super-resolution based on sigmoid transformation
Computer Vision and Pattern Recognition
2017-11-07 v3
Abstract
Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications. As an ill-posed problem, numerous methods have been proposed to reconstruct the missing image details based on exemplars or priors. In this paper, we propose a fast and simple single image super-resolution strategy utilizing patch-wise sigmoid transformation as an imposed sharpening regularization term in the reconstruction, which realizes amazing reconstruction performance. Extensive experiments compared with other state-of-the-art approaches demonstrate the superior effectiveness and efficiency of the proposed algorithm.
Cite
@article{arxiv.1708.07029,
title = {Fast single image super-resolution based on sigmoid transformation},
author = {Longguang Wang and Zaiping Lin and Jinyan Gao and Xinpu Deng and Wei An},
journal= {arXiv preprint arXiv:1708.07029},
year = {2017}
}