盲噪声估计出现在许多图像处理应用的问题，如去噪、COM压缩、分割。在本文中，我们提出了一种新的噪声电平估计方法的基础上的主成分分析的图像块。我们表明，噪声方差可以估计为图像块协方差矩阵的最小特征值。与现有的13种方法相比，所提出的方法显示了良好的速度和精度之间的妥协。这是至少15倍的速度比类似的精度的方法，它是至少两倍的精度比其他方法。我们的方法不假定存在均匀区域的输入图像，因此，可以成功地处理图像只包含纹理。-The problem of blind noise level estimation arises

in many image processing applications, such as denoising, com-

pression, and segmentation. In this paper, we propose a new

noise level estimation method on the basis of principal component

analysis of image blocks. We show that the noise variance can

be estimated as the smallest eigenvalue of the image block

covariance matrix. Compared with 13 existing methods, the

proposed approach shows a good compromise between speed

and accuracy. It is at least 15 times faster than methods with

similar accuracy, and it is at least two times more accurate

than other methods. Our method does not assume the existence

of homogeneous areas in the input image and, hence, can

successfully process images containing only textures.