GPU-aided real-time image/video super resolution based on error feedback

Yuxiang Shen, Xiaolin Wu, Xiaowei Deng

Abstract: Super resolution is a process to generate high-resolution images from their low-resolution versions. In many applications such as super-HD (4K) TV, super resolution has to be performed in real time. In this paper we propose a real-time image/video super-resolution algorithm, which achieves good performance at low computational cost via off-line learning of interpolation errors in different pixel contexts. The proposed algorithm consists of three stages: fast edge-guided interpolation to generate an initial HR estimation, GPU-aided de-convolution, and error feedback compensation. All three stages can be implemented with GPU to support real-time applications. Experiments demonstrate the competitive performance of the new real-time super-resolution algorithm in both PSNR and visual quality.