  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 computational
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
