Blind image super-resolution (SR), aiming to super-resolve low-resolution images with unknown degradation, has attracted increasing attention due to its significance in promoting real-world applications. Many novel and effective solutions have been proposed recently, especially with the powerful deep learning techniques. Despite years of efforts, it still remains as a challenging research problem. This paper serves as a systematic review on recent progress in blind image SR, and proposes a taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used for solving the SR model. This taxonomy helps summarize and distinguish among existing methods. We hope to provide insights into current research states, as well as to reveal novel research directions worth exploring. In addition, we make a summary on commonly used datasets and previous competitions related to blind image SR. Last but not least, a comparison among different methods is provided with detailed analysis on their merits and demerits using both synthetic and real testing images.
翻译:视觉超分辨率(SR)旨在超分辨率的低分辨率图像,而其降解程度却不明,因此,由于在推广真实世界应用方面的重要性,吸引了越来越多的关注。最近提出了许多新颖和有效的解决方案,特别是强有力的深层学习技术。尽管做了多年的努力,它仍然是一个具有挑战性的研究问题。本文件对盲人图像超分辨率(SR)的最新进展进行了系统审查,并提议了一种分类法,将现有方法分类为三种不同类别,根据降解模型的方法和用于解决SR模型的数据进行分类。这一分类法有助于对现有方法进行总结和区分。我们希望提供对当前研究状态的深入了解,并揭示值得探索的新的研究方向。此外,我们还概要介绍了与盲面图像SR有关的常用数据集和以往的竞争情况。最后但并非最不重要的一点是,对不同方法进行比较,同时利用合成和真实测试图像,详细分析其优点和消亡能力。