Real-world single image denoising is crucial and practical in computer
vision. Bayesian inversions combined with score priors now have proven
effective for single image denoising but are limited to white Gaussian noise.
Moreover, applying existing score-based methods for real-world denoising
requires not only the explicit train of score priors on the target domain but
also the careful design of sampling procedures for posterior inference, which
is complicated and impractical. To address these limitations, we propose a
score priors-guided deep variational inference, namely ScoreDVI, for practical
real-world denoising. By considering the deep variational image posterior with
a Gaussian form, score priors are extracted based on easily accessible minimum
MSE Non-$i.i.d$ Gaussian denoisers and variational samples, which in turn
facilitate optimizing the variational image posterior. Such a procedure
adaptively applies cheap score priors to denoising. Additionally, we exploit a
Non-$i.i.d$ Gaussian mixture model and variational noise posterior to model the
real-world noise. This scheme also enables the pixel-wise fusion of multiple
image priors and variational image posteriors. Besides, we develop a
noise-aware prior assignment strategy that dynamically adjusts the weight of
image priors in the optimization. Our method outperforms other single
image-based real-world denoising methods and achieves comparable performance to
dataset-based unsupervised methods.

通过引入得到易用的最小 MSE 非独立同分布高斯去噪器和变分采样，我们提出了一种得分先验引导的深度变分推理方法 ScoreDVI，用于实际场景的去噪。同时，我们利用非独立同分布高斯混合模型和变分噪声后验建模来适应实际场景噪声，并实现多个图像先验和变分图像后验的逐像素融合。此外，我们还开发了一个噪声感知的先验分配策略，动态调整优化中图像先验的权重。我们的方法在实际场景的单图像去噪中表现优于其他方法，且与基于数据集的无监督方法具有相当的性能。