Real-world image denoising is an extremely important image processing problem, which aims to recover clean images from noisy images captured in natural environments. In recent years, diffusion models have achieved very promising results in the field of image generation, outperforming previous generation models. However, it has not been widely used in the field of image denoising because it is difficult to control the appropriate position of the added noise. Inspired by diffusion models, this paper proposes a novel general denoising diffusion model that can be used for real-world image denoising. We introduce a diffusion process with linear interpolation, and the intermediate noisy image is interpolated from the original clean image and the corresponding real-world noisy image, so that this diffusion model can handle the level of added noise. In particular, we also introduce two sampling algorithms for this diffusion model. The first one is a simple sampling procedure defined according to the diffusion process, and the second one targets the problem of the first one and makes a number of improvements. Our experimental results show that our proposed method with a simple CNNs Unet achieves comparable results compared to the Transformer architecture. Both quantitative and qualitative evaluations on real-world denoising benchmarks show that the proposed general diffusion model performs almost as well as against the state-of-the-art methods.

翻译：真实图像去噪是一项极为重要的图像处理问题，其目标是从自然环境中捕获的含噪图像中恢复出干净图像。近年来，扩散模型在图像生成领域取得了极具前景的成果，超越了以往的生成模型。然而，由于难以控制所加噪声的适当位置，该模型尚未广泛用于图像去噪领域。受扩散模型的启发，本文提出了一种新颖的通用去噪扩散模型，可用于真实图像去噪。我们引入了一个带有线性插值的扩散过程，中间带噪图像由原始干净图像与对应真实带噪图像插值得到，从而使该扩散模型能够处理所加噪声的水平。特别地，我们还为该扩散模型引入了两种采样算法。第一种是根据扩散过程定义的简单采样流程，第二种则针对第一种存在的问题进行了多项改进。实验结果表明，我们提出的方法结合简单的CNN Unet架构，就能取得与Transformer架构相当的结果。在真实去噪基准上的定量与定性评估均显示，所提出的通用扩散模型的性能几乎可与最先进的方法相媲美。