Discovering "Semantics" in Super-Resolution Networks

Super-resolution (SR) is a fundamental and representative task of low-level vision area. It is generally thought that the features extracted from the SR network have no specific semantic information, and the network simply learns complex non-linear mappings from input to output. Can we find any "semantics" in SR networks? In this paper, we give affirmative answers to this question. By analyzing the feature representations with dimensionality reduction and visualization, we successfully discover the deep semantic representations in SR networks, \textit{i.e.}, deep degradation representations (DDR), which relate to the image degradation types and degrees. We also reveal the differences in representation semantics between classification and SR networks. Through extensive experiments and analysis, we draw a series of observations and conclusions, which are of great significance for future work, such as interpreting the intrinsic mechanisms of low-level CNN networks and developing new evaluation approaches for blind SR.

Blind Image Super-Resolution: A Survey and Beyond

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.