A Survey of Deep Learning Video Super-Resolution

TL;DR

This paper provides the first comprehensive survey of deep learning-based video super-resolution, categorizing methods, analyzing components, and highlighting trends, challenges, and future directions in the field.

Contribution

It systematically reviews and categorizes deep learning VSR models, establishing a multi-level taxonomy to guide future research and practical applications.

Findings

Identifies key components and technologies in VSR models

Highlights trends and challenges in deep learning VSR research

Provides a taxonomy to classify and analyze VSR methods

Abstract

Video super-resolution (VSR) is a prominent research topic in low-level computer vision, where deep learning technologies have played a significant role. The rapid progress in deep learning and its applications in VSR has led to a proliferation of tools and techniques in the literature. However, the usage of these methods is often not adequately explained, and decisions are primarily driven by quantitative improvements. Given the significance of VSR's potential influence across multiple domains, it is imperative to conduct a comprehensive analysis of the elements and deep learning methodologies employed in VSR research. This methodical analysis will facilitate the informed development of models tailored to specific application needs. In this paper, we present an overarching overview of deep learning-based video super-resolution models, investigating each component and discussing its implications. Furthermore, we provide a synopsis of key components and technologies employed by state-of-the-art and earlier VSR models. By elucidating the underlying methodologies and categorising them systematically, we identified trends, requirements, and challenges in the domain. As a first-of-its-kind survey of deep learning-based VSR models, this work also establishes a multi-level taxonomy to guide current and future VSR research, enhancing the maturation and interpretation of VSR practices for various practical applications.