Deep image denoising models often rely on large amount of training data for the high quality performance. However, it is challenging to obtain sufficient amount of data under real-world scenarios for the supervised training. As such, synthesizing realistic noise becomes an important solution. However, existing techniques have limitations in modeling complex noise distributions, resulting in residual noise and edge artifacts in denoising methods relying on synthetic data. To overcome these challenges, we propose a novel method that synthesizes realistic noise using diffusion models, namely Realistic Noise Synthesize Diffusor (RNSD). In particular, the proposed time-aware controlling module can simulate various environmental conditions under given camera settings. RNSD can incorporate guided multiscale content, such that more realistic noise with spatial correlations can be generated at multiple frequencies. In addition, we construct an inversion mechanism to predict the unknown camera setting, which enables the extension of RNSD to datasets without setting information. Extensive experiments demonstrate that our RNSD method significantly outperforms the existing methods not only in the synthesized noise under multiple realism metrics, but also in the single image denoising performances.

翻译：深度图像去噪模型通常依赖大量训练数据以实现高质量性能。然而，在真实场景下获取充足的有监督训练数据具有挑战性，因此合成真实噪声成为重要解决方案。现有技术在建模复杂噪声分布方面存在局限性，导致依赖合成数据的去噪方法出现残余噪声和边缘伪影。为克服这些挑战，我们提出一种利用扩散模型合成真实噪声的新方法，即真实噪声合成扩散器（RNSD）。具体而言，所提出的时间感知控制模块能够在给定相机设置下模拟多种环境条件。RNSD可融合引导的多尺度内容，从而在多频段生成具有空间相关性的更真实噪声。此外，我们构建了逆映射机制以预测未知的相机设置，使RNSD能够扩展至缺乏设置信息的数据集。大量实验表明，我们的RNSD方法不仅在多项真实感指标下的噪声合成上显著优于现有方法，而且在单图像去噪性能上也表现出明显优势。