  Standard single-image super-resolution creates paired training data from
high-resolution images through fixed downsampling kernels. However, real-world
super-resolution (RWSR) faces unknown degradations in the low-resolution
inputs, all the while lacking paired training data. Existing methods approach
this problem by learning blind general models through complex synthetic
augmentations on training inputs; they sacrifice the performance on specific
degradation for broader generalization to many possible ones. We address the
unsupervised RWSR for a targeted real-world degradation. We study from a
distillation perspective and introduce a novel pairwise distance distillation
framework. Through our framework, a model specialized in synthetic degradation
adapts to target real-world degradations by distilling intra- and inter-model
distances across the specialized model and an auxiliary generalized model.
Experiments on diverse datasets demonstrate that our method significantly
enhances fidelity and perceptual quality, surpassing state-of-the-art
approaches in RWSR. The source code is available at
https://github.com/Yuehan717/PDD.
