  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.
