Liu Anran, Liu Yihao, Gu Jinjin, Qiao Yu, Dong Chao
IEEE Trans Pattern Anal Mach Intell. 2023 May;45(5):5461-5480. doi: 10.1109/TPAMI.2022.3203009. Epub 2023 Apr 3.
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 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 to solve the SR model. This taxonomy helps summarize and distinguish among existing methods. We hope to provide insights into current research states, as well as revealing 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.
盲图像超分辨率（SR）旨在对具有未知退化的低分辨率图像进行超分辨率处理，因其在推动实际应用中的重要性而受到越来越多的关注。最近已经提出了许多新颖且有效的解决方案，特别是借助强大的深度学习技术。尽管经过多年努力，但它仍然是一个具有挑战性的研究问题。本文对盲图像SR的最新进展进行了系统综述，并提出了一种分类法，根据现有方法的退化建模方式和用于求解SR模型的数据，将其分为三个不同的类别。这种分类法有助于总结和区分现有方法。我们希望能深入了解当前的研究状况，并揭示值得探索的新研究方向。此外，我们对与盲图像SR相关的常用数据集和以往竞赛进行了总结。最后但同样重要的是，使用合成测试图像和真实测试图像对不同方法进行了比较，并详细分析了它们的优缺点。
