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- W3109623365 abstract "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/ ." @default.
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- W3109623365 date "2020-01-01" @default.
- W3109623365 modified "2023-10-16" @default.
- W3109623365 title "Learning Camera-Aware Noise Models" @default.
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- W3109623365 doi "https://doi.org/10.1007/978-3-030-58586-0_21" @default.
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