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.

本文介绍了一种新的变分推断方法 - Variational Denoising Network（VDN），该方法将噪声估计和图像去噪综合到一个贝叶斯框架中。使用由深度神经网络参数化的近似后验条件于输入的图像和噪声方差的潜变量，提供具有显式参数形式的后验分布，可用于自动噪声估计的盲图像去噪。VDN 既具有传统模型驱动方法的优点，也具有数据驱动深度学习方法的效率、灵活性和可解释性，且能够在真实场景下估计并消除复杂的非 i.i.d 噪声。综合实验结果表明，该方法在盲图像去噪方面具有优越性。

变分去噪网络：朝向盲噪声建模和去除

Variational Denoising Network: Toward Blind Noise Modeling and Removal

We tackle a challenging blind image denoising problem, in which only single
distinct noisy images are available for training a denoiser, and no information
about noise is known, except for it being zero-mean, additive, and independent
of the clean image. In such a setting, which often occurs in practice, it is
not possible to train a denoiser with the standard discriminative training or
with the recently developed Noise2Noise (N2N) training; the former requires the
underlying clean image for the given noisy image, and the latter requires two
independently realized noisy image pair for a clean image. To that end, we
propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise)
method that first learns a generative model that can 1) simulate the noise in
the given noisy images and 2) generate a rough, noisy estimates of the clean
images, then 3) iteratively trains a denoiser with subsequently synthesized
noisy image pairs (as in N2N), obtained from the generative model. In results,
we show the denoiser trained with our GAN2GAN achieves an impressive denoising
performance on both synthetic and real-world datasets for the blind denoising
setting; it almost approaches the performance of the standard
discriminatively-trained or N2N-trained models that have more information than
ours, and it significantly outperforms the recent baseline for the same
setting, \textit{e.g.}, Noise2Void, and a more conventional yet strong one,
BM3D. The official code of our method is available at
this https URL

本篇研究提出了 GAN2GAN 方法，利用生成式模型生成噪声数据和初始的粗略干净图像估计，并使用由生成式模型合成的噪声图像对去噪器进行迭代训练，结果显示该方法在盲去噪问题上达到了非常好的性能，甚至超越了标准判别式训练或 N2N 训练模型的性能。