@inproceedings{0b9f6696ea78480a9ba4c21d5cdd3d54,
title = "Evaluation on Video Super-Resolution via Self-Supervision-Guided Generative Adversarial Network with Image Denoising",
abstract = "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.",
keywords = "deep learning, denoising, GAN, generative adversarial network, self-supervision, Video super-resolution",
author = "Chen, \{Bo An\} and Kang, \{Li Wei\}",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2025 International Workshop on Advanced Imaging Technology, IWAIT 2025 ; Conference date: 06-01-2025 Through 08-01-2025",
year = "2025",
doi = "10.1117/12.3058013",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Chuan-Yu Chang and Chia-Hung Yeh and Jae-Gon Kim and Kemao Qian and Lau, \{Phooi Yee\}",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2025",
}