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arXiv · 2008.09370

Learning Camera-Aware Noise Models

Ke-Chi Chang·Ren Wang·Hung-Jin Lin·Yu-Lun Liu·Chia-Ping Chen·Yu-Lin Chang·Hwann-Tzong Chen
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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.

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Ke-Chi Chang, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Hwann-Tzong Chen. 2020-08-21. Learning Camera-Aware Noise Models. https://arxiv.org/abs/2008.09370

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