Noisier2Noise: Learning to Denoise from Unpaired Noisy Data
This addresses the challenge of denoising for applications where clean data is unavailable, though it is incremental as it builds on prior noise modeling approaches.
The paper tackles the problem of image denoising without needing clean or paired noisy training data, using only single noisy images and a noise model, and achieves results competitive with methods requiring richer data while outperforming traditional non-learned denoising.
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.