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.

翻译：基于深度学习的方法在单图像去噪中取得了显著性能。然而，训练去噪模型通常需要大量数据，这在现实场景中难以获取。此外，由于真实噪声的复杂性以及生成对抗网络（GAN）模型对噪声分布建模能力不足，过去使用的合成噪声往往与真实噪声存在显著差异，导致去噪模型中残留噪声和伪影。为解决这些问题，我们提出了一种利用扩散模型合成真实噪声的新方法。该方法通过控制相机设置模拟不同环境条件，并采用引导式多尺度内容信息，确保方法能够生成具有多频空间相关性的真实噪声，从而生成大量高质量数据用于训练去噪模型。特别地，我们设计了一种针对相机设置的逆变换机制，使得该方法可扩展至更多无设置信息的公开数据集。基于合成的噪声数据集，我们在多个基准测试上进行了充分实验，结果表明该方法在多项指标上均优于现有最优方法，充分验证了其在合成用于训练去噪模型的真实噪声方面的有效性。