Recently, self-supervised neural networks have shown excellent image denoising performance. However, current dataset free methods are either computationally expensive, require a noise model, or have inadequate image quality. In this work we show that a simple 2-layer network, without any training data or knowledge of the noise distribution, can enable high-quality image denoising at low computational cost. Our approach is motivated by Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise independent noise. Our experiments on artificial, real-world camera, and microscope noise show that our method termed ZS-N2N (Zero Shot Noise2Noise) often outperforms existing dataset-free methods at a reduced cost, making it suitable for use cases with scarce data availability and limited computional resources. A demo of our implementation including our code and hyperparameters can be found in the following colab notebook: https://colab.research.google.com/drive/1i82nyizTdszyHkaHBuKPbWnTzao8HF9b

翻译：近年来，自监督神经网络在图像降噪方面展现出卓越性能。然而，当前无数据集的方法要么计算开销大、需要噪声模型，要么图像质量不足。本研究证明，一个简单的两层网络无需任何训练数据或先验噪声分布知识，即可在低计算成本下实现高质量图像降噪。我们的方法受Noise2Noise和Neighbor2Neighbor启发，对像素级独立噪声的降噪效果良好。在人工噪声、真实相机噪声及显微镜噪声上的实验表明，我们提出的ZS-N2N（零样本噪点到噪点）方法常能以更低成本超越现有无数据集方法，适用于数据稀缺且计算资源受限的场景。实现代码与超参数的演示及使用指南可在以下colab笔记本中获取：https://colab.research.google.com/drive/1i82nyizTdszyHkaHBuKPbWnTzao8HF9b