  Most existing CNN-based super-resolution (SR) methods are developed based on
an assumption that the degradation is fixed and known (e.g., bicubic
downsampling). However, these methods suffer a severe performance drop when the
real degradation is different from their assumption. To handle various unknown
degradations in real-world applications, previous methods rely on degradation
estimation to reconstruct the SR image. Nevertheless, degradation estimation
methods are usually time-consuming and may lead to SR failure due to large
estimation errors. In this paper, we propose an unsupervised degradation
representation learning scheme for blind SR without explicit degradation
estimation. Specifically, we learn abstract representations to distinguish
various degradations in the representation space rather than explicit
estimation in the pixel space. Moreover, we introduce a Degradation-Aware SR
(DASR) network with flexible adaption to various degradations based on the
learned representations. It is demonstrated that our degradation representation
learning scheme can extract discriminative representations to obtain accurate
degradation information. Experiments on both synthetic and real images show
that our network achieves state-of-the-art performance for the blind SR task.
Code is available at: https://github.com/LongguangWang/DASR.
