  Diffusion models (DMs) have recently been introduced in image deblurring and
exhibited promising performance, particularly in terms of details
reconstruction. However, the diffusion model requires a large number of
inference iterations to recover the clean image from pure Gaussian noise, which
consumes massive computational resources. Moreover, the distribution
synthesized by the diffusion model is often misaligned with the target results,
leading to restrictions in distortion-based metrics. To address the above
issues, we propose the Hierarchical Integration Diffusion Model (HI-Diff), for
realistic image deblurring. Specifically, we perform the DM in a highly
compacted latent space to generate the prior feature for the deblurring
process. The deblurring process is implemented by a regression-based method to
obtain better distortion accuracy. Meanwhile, the highly compact latent space
ensures the efficiency of the DM. Furthermore, we design the hierarchical
integration module to fuse the prior into the regression-based model from
multiple scales, enabling better generalization in complex blurry scenarios.
Comprehensive experiments on synthetic and real-world blur datasets demonstrate
that our HI-Diff outperforms state-of-the-art methods. Code and trained models
are available at https://github.com/zhengchen1999/HI-Diff.
