Latest diffusion-based methods for many image restoration tasks outperform
traditional models, but they encounter the long-time inference problem. To
tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM) with an
Efficient Conditional Sampling (ECS) strategy. WaveDM learns the distribution
of clean images in the wavelet domain conditioned on the wavelet spectrum of
degraded images after wavelet transform, which is more time-saving in each step
of sampling than modeling in the spatial domain. In addition, ECS follows the
same procedure as the deterministic implicit sampling in the initial sampling
period and then stops to predict clean images directly, which reduces the
number of total sampling steps to around 5. Evaluations on four benchmark
datasets including image raindrop removal, defocus deblurring, demoir\'eing,
and denoising demonstrate that WaveDM achieves state-of-the-art performance
with the efficiency that is comparable to traditional one-pass methods and over
100 times faster than existing image restoration methods using vanilla
diffusion models