WaveDM: Wavelet-Based Diffusion Models for Image Restoration
- URL: http://arxiv.org/abs/2305.13819v2
- Date: Thu, 25 Jan 2024 11:49:55 GMT
- Title: WaveDM: Wavelet-Based Diffusion Models for Image Restoration
- Authors: Yi Huang, Jiancheng Huang, Jianzhuang Liu, Mingfu Yan, Yu Dong, Jiaxi
Lv, Chaoqi Chen, Shifeng Chen
- Abstract summary: Wavelet-Based Diffusion Model (WaveDM) learns the distribution of clean images in the wavelet domain conditioned on the wavelet spectrum of degraded images after wavelet transform.
WaveDM achieves state-of-the-art performance with the efficiency that is comparable to traditional one-pass methods.
- Score: 43.254438752311714
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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). 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. To
ensure restoration performance, a unique training strategy is proposed where
the low-frequency and high-frequency spectrums are learned using distinct
modules. In addition, an Efficient Conditional Sampling (ECS) strategy is
developed from experiments, which reduces the number of total sampling steps to
around 5. Evaluations on twelve benchmark datasets including image raindrop
removal, rain steaks removal, dehazing, 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.
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