29 August 2016 An image-noise estimation approach using singular value decomposition
Author Affiliations +
Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100331V (2016) https://doi.org/10.1117/12.2243817
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mingfu He, Mingfu He, Mingzhe Liu, Mingzhe Liu, Chengqiang Zhao, Chengqiang Zhao, Jianbo Yang, Jianbo Yang, Helen Zhou, Helen Zhou, } "An image-noise estimation approach using singular value decomposition", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331V (29 August 2016); doi: 10.1117/12.2243817; https://doi.org/10.1117/12.2243817
PROCEEDINGS
6 PAGES


SHARE
Back to Top