Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution
<|reference_start|>Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution: The performance of single image super-resolution depends heavily on how to generate and complement high-frequency details to low-resolution images. Recently, diffusion-based models exhibit great potential in generating high-quality images for super-resolution tasks. However, existing models encounter difficulties in directly predicting high-frequency information of wide bandwidth by solely utilizing the high-resolution ground truth as the target for all sampling timesteps. To tackle this problem and achieve higher-quality super-resolution, we propose a novel Frequency Domain-guided multiscale Diffusion model (FDDiff), which decomposes the high-frequency information complementing process into finer-grained steps. In particular, a wavelet packet-based frequency complement chain is developed to provide multiscale intermediate targets with increasing bandwidth for reverse diffusion process. Then FDDiff guides reverse diffusion process to progressively complement the missing high-frequency details over timesteps. Moreover, we design a multiscale frequency refinement network to predict the required high-frequency components at multiple scales within one unified network. Comprehensive evaluations on popular benchmarks are conducted, and demonstrate that FDDiff outperforms prior generative methods with higher-fidelity super-resolution results.<|reference_end|>
@article{wang2024frequency-domain,
    title={Frequency-Domain Refinement with Multiscale Diffusion for Super
  Resolution},
    author={Xingjian Wang, Li Chai, Jiming Chen},
    journal={arXiv preprint arXiv:2405.10014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10014},
    primaryClass={cs.CV eess.IV}
}