Description: 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.
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