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Removal of PCA Based Estimated Noise in Processed Images

Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 10)

Publication Date:

Authors : ;

Page : 897-899

Keywords : Covariance Matrix; Eigen vectors; Principal Component Analysis; Variance; noise;

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Noise is an important problem in image processing applications. This noise level is to be estimated and is to be removed. Blind noise level estimation is an important image processing step. The proposed system is a new noise level estimation and removal method. It estimates noise based on principal component analysis (PCA) of image blocks. Principal component analysis is one of the statistical techniques frequently used in signal processing to the data dimension reduction or to the data correlation. In PCA first rearrange image blocks into vectors and compute the covariance matrix of these vectors. Then select the covariance matrix eigen values, which correspond only to noise. This allows estimating the noise variance as the average of these eigen values. The blocks to process are selected from image regions with the smallest variance. After noise level estimation the noise is removed using denoise function. It does not require images with homogeneous areas.

Last modified: 2021-06-30 21:10:56