Evaluation on Video Super-Resolution via Self-Supervision-Guided Generative Adversarial Network with Image Denoising

Video super-resolution (VSR) is a technique used for generating high-resolution (HR) frames from the corresponding low-resolution (LR) frames.With the advancement in deep learning techniques and the popularity of high resolution display applications, VSR has drawn much attention in recent years.This paper presents an implementation and evaluation on the classical deep learning-based VSR framework, called TecoGAN (TEmporally COherent GAN), relying on learning temporal coherence via self-supervision for GAN (generative adversarial network)-based video generation.It has been found while applying VSR to enhance the video quality, possibly inherent noises within the frames would be enhanced simultaneously, resulting in unpleasing visual experience.To tackle this problem, we propose to integrate noise removal and VSR for obtaining the noise-removed HR video.Our improved VSR framework has been shown to outperform the original TecoGAN quantitatively and qualitatively.