Single image noise level estimation by artificial noise
Single image noise level estimation by artificial noise
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DOI：
10.1016/j.sigpro.2023.109215
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发表时间：
2023-08-16
影响因子：
4.4
通讯作者：
Zeng，Tieyong
中科院分区：
文献类型：
--
作者：
Li，Fang;Fang，Famin;Zeng，Tieyong
在本文中，我们提出了一种新的噪声级估计方法，该方法是从被加性高斯白噪声和伽马噪声污染的单个噪声图像中估计噪声级，这些噪声通常是由于不同的物理成像系统而出现的。我们的方法是由原始噪声图像和人工噪声图像对应的斑块协方差矩阵的小特征值曲线几乎平行这一关键观察结果激发的。在一定的假设条件下，导出了冗余维噪声图像的特征值与方差之间的比例关系方程。该方程的解给出了噪声估计的基本公式。然而，对于具有纹理和精细细节的自然图像，这些假设并不完全满足。为了克服这个缺点，我们通过引入一个校正参数来修改基本的噪声估计公式。并将该方法推广到乘性噪声估计中。数值结果表明，本文提出的噪声级估计方法在各种场景下的精度都优于现有的先进方法。特别是，测试数据库的均方根误差（RMSE）的平均下降幅度超过10%。我们进一步证明了所提出的噪声估计方法可以用于盲去噪。
In this paper, we propose a new method for noise level estimation from a single noisy image contaminated by additive white Gaussian noise and gamma noise, which usually appear due to different physical imaging systems. Our method is motivated by the key observation that the curves of small eigenvalues of the patch covariance matrices corresponding to the original noisy image and the artificial noisy image are nearly parallel. Based on some assumptions, we derive a proportional relation equation between eigenvalues and variances of the noisy images in the redundant dimensions. The solution of this equation gives the basic noise estimation formula. However, the assumptions are not strictly satisfied for natural images with textures and fine details. To overcome this drawback, we modified the basic noise estimation formula by introducing a correction parameter. The proposed method is also extended to multiplicative Gamma noise estimation. Numerical results demonstrate that the proposed noise level estimation method outperforms the existing state-of-the-art approaches in accuracy for various scenes. In particular, the decreasing of Root Mean Square Error (RMSE) is an average of over 10% for the testing databases. We further demonstrate that the proposed noise estimation method can be used for blind denoising.