Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network's generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of $0.1dB$ , whether trained on or not.

中文翻译：

---

利用高斯噪声水平学习进行盲通用贝叶斯图像去噪


盲通用图像去噪包括使用独特的模型对任何噪声级别的图像进行去噪。它特别实用，因为在开发模型或测试时不需要知道噪声水平。我们提出了一种基于理论的盲通用深度学习图像降噪器，用于加性高斯噪声去除。我们的网络基于最佳去噪解决方案，我们称之为融合去噪。它是用高斯图像先验假设从理论上推导出来的。综合实验显示了我们的网络对看不见的加性噪声​​水平的泛化能力。我们还采用融合去噪网络架构来对真实图像进行图像去噪。我们的方法改进了现实世界的灰度加性图像去噪 PSNR 结果，用于训练噪声水平，并进一步改善训练期间未见的噪声水平。它还将每个噪声级别的最先进的彩色图像去噪性能平均提高了 0.1dB$，无论是否经过训练。