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. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/.
CITATION STYLE
Chang, K. C., Wang, R., Lin, H. J., Liu, Y. L., Chen, C. P., Chang, Y. L., & Chen, H. T. (2020). Learning Camera-Aware Noise Models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12369 LNCS, pp. 343–358). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58586-0_21
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