SVNR：具有去噪扩散的空间变异噪声去除

摘要

去噪扩散模型最近在生成任务中展现出令人印象深刻的结果。通过从大量训练图像集合中学习强大的先验知识，这些模型能够逐渐将完全噪声转换为清晰自然图像，通过一系列小的去噪步骤，似乎使它们非常适合单图像去噪。然而，有效地将去噪扩散模型应用于去除现实噪声比看起来更具挑战性，因为它们的制定基于加性白噪声高斯模型，而不是真实世界图像中的噪声。在这项工作中，我们提出了SVNR，一种新颖的去噪扩散形式，假设更现实的、空间变异的噪声模型。SVNR使得可以使用带噪输入图像作为去噪扩散过程的起点，同时对该过程进行调节。为此，我们调整了扩散过程，使每个像素都有自己的时间嵌入，并提出了支持空间变化时间映射的训练和推理方案。我们的形式化还考虑了存在于条件图像和沿修改后的扩散过程的样本之间的相关性。在实验中，我们展示了我们的方法相对于强大的扩散模型基线以及最先进的单图像去噪方法的优势。

English

Denoising diffusion models have recently shown impressive results in generative tasks. By learning powerful priors from huge collections of training images, such models are able to gradually modify complete noise to a clean natural image via a sequence of small denoising steps, seemingly making them well-suited for single image denoising. However, effectively applying denoising diffusion models to removal of realistic noise is more challenging than it may seem, since their formulation is based on additive white Gaussian noise, unlike noise in real-world images. In this work, we present SVNR, a novel formulation of denoising diffusion that assumes a more realistic, spatially-variant noise model. SVNR enables using the noisy input image as the starting point for the denoising diffusion process, in addition to conditioning the process on it. To this end, we adapt the diffusion process to allow each pixel to have its own time embedding, and propose training and inference schemes that support spatially-varying time maps. Our formulation also accounts for the correlation that exists between the condition image and the samples along the modified diffusion process. In our experiments we demonstrate the advantages of our approach over a strong diffusion model baseline, as well as over a state-of-the-art single image denoising method.

SVNR：具有去噪扩散的空间变异噪声去除

SVNR: Spatially-variant Noise Removal with Denoising Diffusion

摘要

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