论文标题
嘈杂图像中的目标意识到的泊松高斯噪声参数估计
Target Aware Poisson-Gaussian Noise Parameters Estimation from Noisy Images
论文作者
论文摘要
在许多情况下，数字传感器可以导致嘈杂的结果。为了能够从图像中删除不需要的噪声，正确的噪声建模和准确的噪声参数估计至关重要。在这个项目中，我们使用传感器捕获的原始图像的泊松高斯噪声模型，因为它非常适合传感器的物理特性。此外，我们将自己限制在观察到（嘈杂）和地面（无噪声）图像对的情况下。使用这种对对噪声估计是有益的，在文献中并未广泛研究。基于此模型，我们得出理论最大似然解决方案，讨论其实际实现和优化。此外，我们提出了基于方差和累积统计数据的两种算法。最后，我们将方法的结果与两种不同的方法进行了比较，我们对自己进行了训练的CNN，另一种是从文献中获取的。所有这些方法之间的比较表明，我们的算法在MSE方面优于其他算法，并且具有良好的其他属性。
Digital sensors can lead to noisy results under many circumstances. To be able to remove the undesired noise from images, proper noise modeling and an accurate noise parameter estimation is crucial. In this project, we use a Poisson-Gaussian noise model for the raw-images captured by the sensor, as it fits the physical characteristics of the sensor closely. Moreover, we limit ourselves to the case where observed (noisy), and ground-truth (noise-free) image pairs are available. Using such pairs is beneficial for the noise estimation and is not widely studied in literature. Based on this model, we derive the theoretical maximum likelihood solution, discuss its practical implementation and optimization. Further, we propose two algorithms based on variance and cumulant statistics. Finally, we compare the results of our methods with two different approaches, a CNN we trained ourselves, and another one taken from literature. The comparison between all these methods shows that our algorithms outperform the others in terms of MSE and have good additional properties.
