Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising.

翻译：盲图像去噪是计算机视觉中一个重要但极具挑战性的问题，原因在于真实图像的采集过程非常复杂。本文提出了一种新的变分推断方法，将噪声估计与图像去噪整合到一个统一的贝叶斯框架中，用于解决盲图像去噪问题。具体而言，我们通过深度神经网络参数化一个近似后验分布，将内在的干净图像和噪声方差作为以输入含噪图像为条件的潜在变量。该后验分布为其所有涉及的超参数提供了明确的参数化形式，因此可以轻松实现带有自动噪声估计的盲图像去噪，用于测试含噪图像。一方面，与其他数据驱动的深度学习方法类似，我们的方法——即变分去噪网络（VDN）——由于后验表达式的显式形式，能够高效地进行去噪。另一方面，VDN继承了传统模型驱动方法的优势，特别是生成模型良好的泛化能力。VDN具有良好的可解释性，并且可以灵活地用于估计和去除真实场景中收集到的复杂非独立同分布噪声。我们进行了全面的实验，以验证我们的方法在盲图像去噪中的优越性。