Towards Realistic Data Generation for Real-World Super-Resolution
Keywords: Real-world Image Super-Resolution; Data Generation
TL;DR: We propose a novel unsupervised RealDGen to adaptively generate large-scale, realistic, and diverse data for real-world super-resolution, significantly enhancing the generalization ability and performance of popular SR models.
Abstract: Existing image super-resolution (SR) techniques often fail to generalize effectively in complex real-world settings due to the significant divergence between training data and practical scenarios. To address this challenge, previous efforts have either manually simulated intricate physical-based degradations or utilized learning-based techniques, yet these approaches remain inadequate for producing large-scale, realistic, and diverse data simultaneously. In this paper, we introduce a novel Realistic Decoupled Data Generator (RealDGen), an unsupervised learning data generation framework designed for real-world super-resolution. We meticulously develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model to create realistic low-resolution images from unpaired real LR and HR images. Extensive experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations, significantly advancing the performance of popular SR models on various real-world benchmarks.
Primary Area: generative models
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Submission Number: 4951
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