  In supervised learning for image denoising, usually the paired clean images
and noisy images are collected or synthesised to train a denoising model. L2
norm loss or other distance functions are used as the objective function for
training. It often leads to an over-smooth result with less image details. In
this paper, we regard the denoising task as a problem of estimating the
posterior distribution of clean images conditioned on noisy images. We apply
the idea of diffusion model to realize generative image denoising. According to
the noise model in denoising tasks, we redefine the diffusion process such that
it is different from the original one. Hence, the sampling of the posterior
distribution is a reverse process of dozens of steps from the noisy image. We
consider three types of noise model, Gaussian, Gamma and Poisson noise. With
the guarantee of theory, we derive a unified strategy for model training. Our
method is verified through experiments on three types of noise models and
achieves excellent performance.

Computer Vision and Pattern Recognition