College of Computer and Information Science, Chongqing Normal University, Chongqing, China

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Abstract—Over the years a variety of methods have been introduced to remove noise from digital images, such as Gaussian filtering, anisotropic filtering, and Total Variation minimization. However, many of these algorithms remove the fine details and structure of the image in addition to the noise because of assumptions made about the frequency content of the image. It is analyzed in the way that the noise is decomposed into low and high frequency sub-band under the wavelet transformation, and subconsequently extracts the principle components feature with the method of Principle Component Analysis(PCA). This can keep the the picture as detailed as possible, while at the same time getting rid of the noise. This exeperiment proves that this method can get rid of the noise of the picture not only effectively, but also keeps the detail of the picture to the maximum.

Index Terms—image denoising, integer wavelet, Principle Component Analysi

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Cite: Jing Peng and Yan Ma, "Integer Wavelet Image Denoising Method Based on Principle Component Analysis," Journal of Software vol. 7, no. 5, pp. 982-989, 2012.