  The problem of blind image super-resolution aims to recover high-resolution
(HR) images from low-resolution (LR) images with unknown degradation modes.
Most existing methods model the image degradation process using blur kernels.
However, this explicit modeling approach struggles to cover the complex and
varied degradation processes encountered in the real world, such as high-order
combinations of JPEG compression, blur, and noise. Implicit modeling for the
degradation process can effectively overcome this issue, but a key challenge of
implicit modeling is the lack of accurate ground truth labels for the
degradation process to conduct supervised training. To overcome this
limitations inherent in implicit modeling, we propose an
\textbf{U}ncertainty-based degradation representation for blind
\textbf{S}uper-\textbf{R}esolution framework (\textbf{USR}). By suppressing the
uncertainty of local degradation representations in images, USR facilitated
self-supervised learning of degradation representations. The USR consists of
two components: Adaptive Uncertainty-Aware Degradation Extraction (AUDE) and a
feature extraction network composed of Variable Depth Dynamic Convolution
(VDDC) blocks. To extract Uncertainty-based Degradation Representation from LR
images, the AUDE utilizes the Self-supervised Uncertainty Contrast module with
Uncertainty Suppression Loss to suppress the inherent model uncertainty of the
Degradation Extractor. Furthermore, VDDC block integrates degradation
information through dynamic convolution. Rhe VDDC also employs an Adaptive
Intensity Scaling operation that adaptively adjusts the degradation
representation according to the network hierarchy, thereby facilitating the
effective integration of degradation information. Quantitative and qualitative
experiments affirm the superiority of our approach.
