  Image deblurring is an ill-posed problem with multiple plausible solutions
for a given input image. However, most existing methods produce a deterministic
estimate of the clean image and are trained to minimize pixel-level distortion.
These metrics are known to be poorly correlated with human perception, and
often lead to unrealistic reconstructions. We present an alternative framework
for blind deblurring based on conditional diffusion models. Unlike existing
techniques, we train a stochastic sampler that refines the output of a
deterministic predictor and is capable of producing a diverse set of plausible
reconstructions for a given input. This leads to a significant improvement in
perceptual quality over existing state-of-the-art methods across multiple
standard benchmarks. Our predict-and-refine approach also enables much more
efficient sampling compared to typical diffusion models. Combined with a
carefully tuned network architecture and inference procedure, our method is
competitive in terms of distortion metrics such as PSNR. These results show
clear benefits of our diffusion-based method for deblurring and challenge the
widely used strategy of producing a single, deterministic reconstruction.
