  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.
