In supervised image restoration tasks, one key issue is how to obtain the aligned high-quality (HQ) and low-quality (LQ) training image pairs. Unfortunately, such HQ-LQ training pairs are hard to capture in practice, and hard to synthesize due to the complex unknown degradation in the wild. While several sophisticated degradation models have been manually designed to synthesize LQ images from their HQ counterparts, the distribution gap between the synthesized and real-world LQ images remains large. We propose a new approach to synthesizing realistic image restoration training pairs using the emerging denoising diffusion probabilistic model (DDPM). First, we train a DDPM, which could convert a noisy input into the desired LQ image, with a large amount of collected LQ images, which define the target data distribution. Then, for a given HQ image, we synthesize an initial LQ image by using an off-the-shelf degradation model, and iteratively add proper Gaussian noises to it. Finally, we denoise the noisy LQ image using the pre-trained DDPM to obtain the final LQ image, which falls into the target distribution of real-world LQ images. Thanks to the strong capability of DDPM in distribution approximation, the synthesized HQ-LQ image pairs can be used to train robust models for real-world image restoration tasks, such as blind face image restoration and blind image super-resolution. Experiments demonstrated the superiority of our proposed approach to existing degradation models. Code and data will be released.

翻译：在有监督的图像恢复任务中，一个关键问题是如何获取对齐的高质量（HQ）和低质量（LQ）训练图像对。遗憾的是，这种HQ-LQ训练对在实际中难以捕捉，且由于野生环境中复杂的未知退化而难以合成。尽管已经手动设计了多种复杂的退化模型来从HQ图像合成LQ图像，但合成LQ图像与真实LQ图像之间的分布差距仍然很大。我们提出了一种新方法，利用新兴的去噪扩散概率模型（DDPM）来合成真实的图像恢复训练对。首先，我们使用大量收集的LQ图像训练一个DDPM，该模型能将带噪声的输入转换为期望的LQ图像，从而定义目标数据分布。然后，对于给定的HQ图像，我们通过现成的退化模型合成初始LQ图像，并迭代添加适当的高斯噪声。最后，我们使用预训练的DDPM对带噪声的LQ图像进行去噪，得到最终LQ图像，该图像落入真实LQ图像的目标分布中。得益于DDPM在分布逼近方面的强大能力，合成的HQ-LQ图像对可用于训练鲁棒模型，以应对真实世界的图像恢复任务，如盲人脸图像恢复和盲图像超分辨率。实验证明了我们提出的方法相对于现有退化模型的优越性。代码和数据将公开发布。