Real-World Denoising via Diffusion Model
Cheng Yang Lijing Liang Zhixun Su
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.