Towards Realistic Data Generation for Real-World Super-Resolution
<|reference_start|>Towards Realistic Data Generation for Real-World Super-Resolution: 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.<|reference_end|>
@article{peng2024towards,
    title={Towards Realistic Data Generation for Real-World Super-Resolution},
    author={Long Peng, Wenbo Li, Renjing Pei, Jingjing Ren, Yang Wang, Yang Cao,
  Zheng-Jun Zha},
    journal={arXiv preprint arXiv:2406.07255},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07255},
    primaryClass={cs.CV eess.IV}
}