Deep learning-based approaches have achieved remarkable performance in
single-image denoising. However, training denoising models typically requires a
large amount of data, which can be difficult to obtain in real-world scenarios.
Furthermore, synthetic noise used in the past has often produced significant
differences compared to real-world noise due to the complexity of the latter
and the poor modeling ability of noise distributions of Generative Adversarial
Network (GAN) models, resulting in residual noise and artifacts within
denoising models. To address these challenges, we propose a novel method for
synthesizing realistic noise using diffusion models. This approach enables us
to generate large amounts of high-quality data for training denoising models by
controlling camera settings to simulate different environmental conditions and
employing guided multi-scale content information to ensure that our method is
more capable of generating real noise with multi-frequency spatial
correlations. In particular, we design an inversion mechanism for the setting,
which extends our method to more public datasets without setting information.
Based on the noise dataset we synthesized, we have conducted sufficient
experiments on multiple benchmarks, and experimental results demonstrate that
our method outperforms state-of-the-art methods on multiple benchmarks and
metrics, demonstrating its effectiveness in synthesizing realistic noise for
training denoising models.

该研究论文提出了一种基于扩散模型的合成逼真噪声的新方法，用于为困难获得真实数据的情景下的训练去噪模型提供大量高质量的数据，并在多个基准测试上证明了其方法的优越性.