  Diffusion Probabilistic Models (DPMs) have recently been employed for image
deblurring, formulated as an image-conditioned generation process that maps
Gaussian noise to the high-quality image, conditioned on the blurry input.
Image-conditioned DPMs (icDPMs) have shown more realistic results than
regression-based methods when trained on pairwise in-domain data. However,
their robustness in restoring images is unclear when presented with
out-of-domain images as they do not impose specific degradation models or
intermediate constraints. To this end, we introduce a simple yet effective
multiscale structure guidance as an implicit bias that informs the icDPM about
the coarse structure of the sharp image at the intermediate layers. This guided
formulation leads to a significant improvement of the deblurring results,
particularly on unseen domain. The guidance is extracted from the latent space
of a regression network trained to predict the clean-sharp target at multiple
lower resolutions, thus maintaining the most salient sharp structures. With
both the blurry input and multiscale guidance, the icDPM model can better
understand the blur and recover the clean image. We evaluate a single-dataset
trained model on diverse datasets and demonstrate more robust deblurring
results with fewer artifacts on unseen data. Our method outperforms existing
baselines, achieving state-of-the-art perceptual quality while keeping
competitive distortion metrics.
