  Modeling imaging sensor noise is a fundamental problem for image processing
and computer vision applications. While most previous works adopt statistical
noise models, real-world noise is far more complicated and beyond what these
models can describe. To tackle this issue, we propose a data-driven approach,
where a generative noise model is learned from real-world noise. The proposed
noise model is camera-aware, that is, different noise characteristics of
different camera sensors can be learned simultaneously, and a single learned
noise model can generate different noise for different camera sensors.
Experimental results show that our method quantitatively and qualitatively
outperforms existing statistical noise models and learning-based methods.
