Learning Camera-Aware Noise Models

• URL: http://arxiv.org/abs/2008.09370v1
• Date: Fri, 21 Aug 2020 08:25:14 GMT
• Title: Learning Camera-Aware Noise Models
• Authors: Ke-Chi Chang, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Hwann-Tzong Chen
• Abstract summary: 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.
• Score: 22.114167097784787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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