采用高阶多项式局部表面近似和统计推断方法精确估计图像噪声水平

Tingting Kou, Lei Yang, Y. Wan
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引用次数: 1

摘要

图像噪声水平估计是图像去噪、压缩和分割等许多图像处理任务的重要步骤。尽管最近提出的SVD和PCA方法产生了迄今为止最准确的估计,但这些基于线性子空间的方法仍然受到干净信号内容的信号污染,特别是在低噪声情况下。此外,目前使用的常见性能评估程序将测试图像视为无噪声。这忽略了那些测试图像中已经存在的噪声,并且总是会产生偏差。在本文中,我们做了两个贡献。首先,我们提出了一种新的基于非线性局部曲面近似的噪声级估计方法。在该方法中,我们首先使用高次多项式近似每个块中的图像无噪声内容。然后对遵循卡方分布的块残差进行排序,并使用精心选择的大小的上分位数进行估计。其次,我们提出了一种新的性能评估程序,该程序不受测试图像中已经存在的噪声的影响。实验结果表明,该方法在估计精度和稳定性方面都比目前常用的方法有很大的提高。
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Accurate image noise level estimation by high order polynomial local surface approximation and statistical inference
Image noise level estimation is an important step in many image processing tasks such as denoising, compression and segmentation. Although recently proposed SVD and PCA approaches have produced the most accurate estimates so far, these linear subspace-based methods still suffer from signal contamination from the clean signal content, especially in the low noise situation. In addition, the common performance evaluation procedure currently in use treats test images as noise-free. This omits the noise already in those test images and invariably incurs a bias. In this paper we make two contributions. First, we propose a new noise level estimation method using nonlinear local surface approximation. In this method, we first approximate image noise-free content in each block using a high degree polynomial. Then the block residual variances, which follow chi squared distribution, are sorted and the upper quantile of a carefully chosen size is used for estimation. Secondly, we propose a new performance evaluation procedure that is free from the influence of the noise already present in the test images. Experimental results show that it has much improved performance than typical state-of-the-art methods in terms of both estimation accuracy and stability.
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