论文标题
学习摄像头吸引噪声模型
Learning Camera-Aware Noise Models
论文作者
论文摘要
建模成像传感器噪声是图像处理和计算机视觉应用的基本问题。尽管大多数以前的作品都采用统计噪声模型，但实际噪声要复杂得多，并且超出了这些模型所描述的内容。为了解决这个问题，我们提出了一种数据驱动的方法，其中从现实世界的噪声中学到了生成噪声模型。提出的噪声模型是相机感知的，也就是说，可以同时学习不同摄像机传感器的不同噪声特性，并且单个学习的噪声模型可以为不同的摄像机传感器产生不同的噪声。实验结果表明，我们的方法在定量上和定性上都优于现有的统计噪声模型和基于学习的方法。
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.
