  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.
