  Learning-based image denoising methods have been bounded to situations where
well-aligned noisy and clean images are given, or samples are synthesized from
predetermined noise models, e.g., Gaussian. While recent generative noise
modeling methods aim to simulate the unknown distribution of real-world noise,
several limitations still exist. In a practical scenario, a noise generator
should learn to simulate the general and complex noise distribution without
using paired noisy and clean images. However, since existing methods are
constructed on the unrealistic assumption of real-world noise, they tend to
generate implausible patterns and cannot express complicated noise maps.
Therefore, we introduce a Clean-to-Noisy image generation framework, namely
C2N, to imitate complex real-world noise without using any paired examples. We
construct the noise generator in C2N accordingly with each component of
real-world noise characteristics to express a wide range of noise accurately.
Combined with our C2N, conventional denoising CNNs can be trained to outperform
existing unsupervised methods on challenging real-world benchmarks by a large
margin.
