Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution

• URL: http://arxiv.org/abs/2407.07302v1
• Date: Wed, 10 Jul 2024 01:46:40 GMT
• Title: Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution
• Authors: Yuehan Zhang, Seungjun Lee, Angela Yao,
• Abstract summary: 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. We introduce a novel pairwise distance distillation framework to address the unsupervised RWSR for a targeted real-world degradation.
• Score: 38.79439380482431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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