Image denoising is a fundamental problem in computational photography, where
achieving high-quality perceptual performance with low distortion is highly
demanding. Current methods either struggle with perceptual performance or
suffer from significant distortion. Recently, the emerging diffusion model
achieves state-of-the-art performance in various tasks, and its denoising
mechanism demonstrates great potential for image denoising. However,
stimulating diffusion models for image denoising is not straightforward and
requires solving several critical problems. On the one hand, the input
inconsistency hinders the connection of diffusion models and image denoising.
On the other hand, the content inconsistency between the generated image and
the desired denoised image introduces additional distortion. To tackle these
problems, we present a novel strategy called Diffusion Model for Image
Denoising (DMID) by understanding and rethinking the diffusion model from a
denoising perspective. Our DMID strategy includes an adaptive embedding method
that embeds the noisy image into a pre-trained diffusion model, and an adaptive
ensembling method that reduces distortion in the denoised image. Our DMID
strategy achieves state-of-the-art performance on all distortion-based and
perceptual metrics, for both Gaussian and real-world image denoising.

图像降噪是计算摄影中的一个基本问题，要求在低畸变的情况下实现高质量的感知性能。最近，新兴的扩散模型在各种任务中实现了最先进的性能，在图像降噪方面显示了巨大的潜力。然而，为图像降噪刺激扩散模型并不直接，需要解决几个关键问题。我们提出了一种名为 “图像降噪扩散模型（DMID）” 的新策略，通过从降噪的角度理解和重新思考扩散模型来解决这些问题。我们的 DMID 策略在所有基于畸变和感知度量的高斯和真实图像降噪任务中实现了最先进的性能。