Learning Camera Aware Noise Models
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.
ECCV