A review of deep-learning-based super-resolution: From methods to applications

Super-resolution (SR), aiming to super-resolve degraded low-resolution image to recover the corresponding high-resolution counterpart, is an important and challenging task in computer vision, and with various applications. The emergence of deep learning (DL) has significantly advanced SR methods, surpassing the performance of traditional techniques. This paper presents a comprehensive survey of DL-based SR methods encompassing single image super resolution (SISR) and multiple image super resolution (MISR) methods, along with their applications and limitations. In SISR methods, addressing individual images independently, we review blind and non-blind SR methods. Additionally, within MISR, we delve into multi-frame, multi-view, and reference-based SR methods. DL-based SR methods are categorized from the application perspective and a taxonomy is proposed. Finally, we present research prospects and future directions.