An Image-Noise Estimation Approach Using Singular Value Decomposition

摘要

This paper proposes a simple and accurate estimation of the additive white Gaussian noise for the noise-contaminated digital images. One can easily estimate the noise level through singular value decomposition (SVD) to the noise-polluted image if an image is deteriorated by the additive white Gaussian noise. As described in the paper, the sum of some specific singular values has the linear relationship with the standard deviation of noise. Based on no correlation between noises, we add known noises upon a noise image. Then noise level is estimated by solving a nonlinear over-determined matrix equation. The proposed algorithm was experimentally tested by the benchmark images and outperforms estimation method of selecting weak textured patches using principal component analysis (PCA). The proposed method is more independent on the original image information and presents a higher accuracy and a stronger robustness for a range of noise level in various images.