Score Priors Guided Deep Variational Inference for Unsupervised
  Real-World Single Image Denoising
Jun Cheng Tao Liu Shan Tan
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.