期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2012
卷号：3
期号：2
页码：236
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Principal component analysis (PCA) is an orthogonal transformation that seeks thedirections 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 significantimage details. PCA is a statistical technique for simplifying a dataset by reducing datasets to lowerdimensions. It is a standard technique commonly used for data reduction in statistical pattern recognitionand 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 tofurther improve the denoising performance, and the noise level is adaptively adjusted in the second stage.
关键词：Principal component analysis; local pixel grouping; denoising; filter and discrete;wavelet transform.
