Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
This provides an efficient solution for denoising in scenarios with scarce data and limited computational resources, though it is incremental as it builds on prior self-supervised methods.
The paper tackles the problem of image denoising without training data or noise models, achieving high-quality results at low computational cost, with experiments showing it often outperforms existing dataset-free methods.
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