Abstract
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption. In practice, we show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans -- all corrupted by different processes -- based on noisy data only.
Abstract (translated)
我们将基本的统计推理应用于通过机器学习进行信号重建 - 学习将损坏的观察结果映射到干净的信号 - 得出一个简单而有力的结论：通过仅查看损坏的示例，有时在性能上，有时可以学习恢复图像超出使用干净数据的培训，没有明确的图像先验或腐败的可能性模型。在实践中，我们展示了单个模型学习了摄影噪声去除，去噪合成蒙特卡罗图像，以及重建欠采样MRI扫描 - 所有这些都被不同的过程破坏 - 仅基于噪声数据。
URL
https://arxiv.org/abs/1803.04189
