PCA based image denoising
Y. Murali Mohan Babu · Signal & Image Processing An International Journal · 2012
Principal component analysis (PCA) is an orthogonal transformation that seeks the directions of maximum variance in the data and is commonly used to reduce the dimensionality of the data.In image denoising, a compromise has to be found between noise reduction and preserving significant image details.PCA is a statistical technique for simplifying a dataset by reducing datasets to lower dimensions.It is a standard technique commonly used for data reduction in statistical pattern recognition and signal processing.This paper proposes a denoising technique by using a new statistical approach, principal component analysis with local pixel grouping (LPG).This procedure is iterated second time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage.