A new diffusion method for blind image denoising

Image denoising is a significant task in computer vision. Previous studies have mostly concentrated on removing noise with specific levels. The blind image denoising approach has recently gained more popularity due to its adaptability. Nonetheless, existing deep learning methods only train networks to learn the direct projection from noisy images to clean ones, which limits their denoising performance. This paper proposes a novel perspective for blind denoising by converting the static image denoising problem into a dynamic process inspired by the diffusion model. To achieve this, we present a new method that views a noisy image as a mid-state of a Gaussian diffusion process. Specifically, the image noise is separated into multiple sub-level noises through the diffusion process, and subsequently eliminated in a sequential manner. Furthermore, we propose a diffusion denoising network that comprises a Feature Extraction Module for extracting image features and a Diffusion Noise Estimation Module for estimating the sub-level noises. Our experiments demonstrate that our proposed method outperforms existing methods and achieves state-of-the-art results in blind additive white Gaussian noise and real-world image denoising.