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.

翻译：最新的基于扩散方法的图像恢复任务在许多方面超越了传统模型，但面临长时间推理的问题。为解决这一问题，本文提出了一种基于小波的扩散模型（WaveDM），并附带高效条件采样（ECS）策略。WaveDM在小波域中学习干净图像的分布，该分布以小波变换后退化图像的小波谱为条件，相比在空间域建模，每个采样步骤更节省时间。此外，ECS在初始采样阶段遵循与确定性隐式采样相同的流程，随后停止并直接预测干净图像，从而将总采样步骤减少至约5步。在四个基准数据集上的评估，包括图像雨滴去除、散焦去模糊、去摩尔纹和去噪，表明WaveDM实现了最先进的性能，其效率与传统一步方法相当，且比使用原始扩散模型的现有图像恢复方法快100倍以上。