We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise.

本论文提出了训练神经网络进行图像去噪的方法，该方法无需干净训练数据或带噪训练数据。该方法仅需要每个训练样本的单个噪声实现和噪声分布的统计模型，并适用于各种噪声模型，包括具有空间结构的噪声。该模型的表现与需要更多训练数据的其他学习方法相当，且优于传统非学习去噪方法。本文提出了我们的方法对于任意加性噪声的推导，对于特定的高斯加性噪声的改进，以及对于乘性伯努利噪声的扩展。

从不成对嘈杂数据中学习降噪的Noisier2Noise