Noisy video super-resolution

Feng Liu, Jinjun Wang, Shenghuo Zhu, Michael Gleicher, Yihong Gong

Abstract: Low-quality videos often not only have limited resolution, but also suffer from noise. Directly up-sampling a video without considering noise could deteriorate its visual quality due to magnifying noise. This paper addresses this problem with a unified framework that achieves simultaneous de-noising and super-resolution. This framework formulates noisy video super-resolution as an optimization problem, aiming to maximize the visual quality of the result. We consider a good quality result to be fidelity-preserving, detail-preserving and smooth. Accordingly, we propose measures for these qualities in the scenario of de-noising and super-resolution. The experiments on a variety of noisy videos demonstrate the effectiveness of the presented algorithm.