Noisier2Noise: Learning to Denoise From Unpaired Noisy Data

CVPR 2020

Noisier2Noise: Learning to Denoise From Unpaired Noisy Data

Sep 29, 2020
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Authors: Nick Moran, Dan Schmidt, Yu Zhong, Patrick Coady Description: 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.

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