We propose a fully-convolutional neural-network architecture for image
denoising which is simple yet powerful. Its structure allows to exploit the
gradual nature of the denoising process, in which shallow layers handle local
noise statistics, while deeper layers recover edges and enhance textures. Our
method advances the state-of-the-art when trained for different noise levels
and distributions (both Gaussian and Poisson). In addition, we show that making
the denoiser class-aware by exploiting semantic class information boosts
performance, enhances textures and reduces artifacts.

本文提出了一种基于全卷积神经网络的图像去噪方法，既简单又强大，可以应对不同噪声水平和分布（高斯和泊松），并且利用语义类信息实现了类感知。

面向类别的全卷积高斯和泊松去噪

Class-Aware Fully-Convolutional Gaussian and Poisson Denoising

We report closed-form formula for calculating the Chi square and higher-order
Chi distances between statistical distributions belonging to the same
exponential family with affine natural space, and instantiate those formula for
the Poisson and isotropic Gaussian families. We then describe an analytic
formula for the $f$-divergences based on Taylor expansions and relying on an
extended class of Chi-type distances.

给出了一种计算同一指数族统计分布之间 Chi 平方和高阶 Chi 距离的封闭式公式，并针对泊松分布和各向同性高斯分布进行了实例化。然后，我们描述了一种基于泰勒展开和依赖于扩展 Chi 类型距离的 $f$-divergences 的解析公式。