Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution
<|reference_start|>Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution: 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.<|reference_end|>
@article{zhang2024pairwise,
    title={Pairwise Distance Distillation for Unsupervised Real-World Image
  Super-Resolution},
    author={Yuehan Zhang, Seungjun Lee and Angela Yao},
    journal={arXiv preprint arXiv:2407.07302},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07302},
    primaryClass={eess.IV cs.CV}
}