[Sekhar* et al., 5(6): June, 2016] ISSN: 2277-9655 IC™ Value: 3.00 Impact Factor: 4.116 http: // www.ijesrt.com © International Journal of Engineering Sciences & Research Technology [673] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY PRINCIPAL COMPONENT ANALYSIS BASED IMAGE DENOISING IMPLEMENTED USING LPG AND COMPARED TO WAVELET TRANSFORM TECHNIQUES B.V.D.S.Sekhar * , Dr.P.V.G.D.Prasad Reddy, Dr.G.P.S.Varma * Research Scholar, Department of CS&SE, Andhra University, Visakghapatnam Department of CS&SE, Andhra University, Visakghapatnam Dept. of I.T, SRKR Engineering College, Bhimavaram DOI: 10.5281/zenodo.55803 ABSTRACT Removal of noise is an important step in the image restoration process, but denoising of image remains a challenging problem in recent research associate with image processing. Denoising is used to remove the noise from corrupted image, while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. We propose an efficient image denoising technique using wavelet based principal component analysis(PCA) with local pixel grouping(LPG).For a better preservation of image local structures, a pixel and its nearest neighbors are modelled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. This method compares PSNR (Peak signal to noise ratio) between original image and noisy image and PSNR between original image and denoised image. The MSE and PSNR of the proposed method and local adaptive wavelet image denoising method are compared and demonstrated. Therefore, the image after denoising has a better visual effect. KEYWORDS: PCA, Denoising, LPG. INTRODUCTION Information transmitted in the form of digital images is becoming a major method of communication in the modern age. An image is often corrupted by noise in its acquition and transmission. The received image needs processing before it can be used in applications. ‘Image Denoising’ involves the manipulation of the image data to produce a visually high quality image. Image denoising is used to remove the additive noise while retaining as much as possible the important signal features. In the recent years there has been a fair amount of research on wavelet thresholding and threshold selection for signal de-noising [1], [2],[3], [4], because wavelet provides an appropriate basis for separating noisy signal from the image signal. As a primary low-level image processing procedure, noise removal has been extensively studied and many denoising schemes have been proposed, from the earlier smoothing filters and frequency domain denoising methods [5] to the lately developed wavelet [1 –10], curvelet [11] and ridgelet [6] based methods, sparse representation [7] and K-SVD [8] methods, shape-adaptive transform [15], bilateral filtering [10,11], non-local mean based methods [12,13] and non-local collaborative filtering [14]. Wavelet transform (WT) decomposes the input signal into multiple scales, which represent different time-frequency components of the original signal. At each scale thresholding [15,16] and statistical modeling [17-19], can be per- formed to suppress noise. The processed wavelet coefficients are transformed back into spatial domain by denoising. To represent the image wavelet transform uses a fixed wavelet basis with dilation and translation. Wavelet transform can cause distortion in the denoising output. To overcome the drawback in wavelet transform, a spatially adaptive principal component analysis (PCA) is used which computes the locally fitted basis to transform the image and a shape-adaptive discrete cosine transform (DCT) to the neighborhood, which can achieve very sparse representation of the image leading to effective denoising. PCA is a useful statistical technique that has found application in fields such as face recognition and image compression,