文章基本信息

标题：Noise Removal in Images in the Non Subsampled Contourlet Transform Domain Using Orthogonal Matching Pursuit
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作者：Divya V ; Dr. Sasikumar M
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：6
DOI：10.15680/ijircce.2015.0306062
出版社：S&S Publications
摘要：Developing an efficient method of removing noise from digital images before processing them forfurther analysis, is a significant process in image processing. Ample algorithms are available, but they work well undercertain assumptions, and has pros and cons. In this paper, a technique of noise removal from digital images is proposedwhere the image is first transformed to the nonsubsampled contourlet transform (NSCT) domain and then supportvector machine (SVM) is used for classifying noisy pixels from the edge related ones. The spatial relationships betweenpixels in the original image are well represented by the coefficients in the NSCT domain. These spatial relationshipsrepresent features of the image and should be retained as much as possible during denoising. The NSCT detailcoefficients are extracted and feature vector for a pixel in the noisy image is formed by the spatial regularity in NSCTdomain. Then the SVM model is obtained by training, and the NSCT detail coefficients are classified into two classes(noise related coefficients and non-noisy ones) using the model. Finally, the denoising is done by the orthogonalmatching pursuit algorithm (OMP), which is an iterative greedy algorithm that finds the most correlated estimate of thedenoised image by minimizing the residuals at each step. The proposed method has the advantage of achieving a goodvisual quality with very less quantity of disturbing artifacts. The method utilizes the directional properties of NSCT topreserve the information bearing structures such as edges and the excellent classification properties of SVM to classifythe noisy pixels from the non-noisy ones. Objective evaluations such as peak signal to noise ratio (PSNR), structuralsimilarity index (SSIM) and root mean square error (RMSE), reveal the superiority of the proposed method over thestate of art denoising algorithms.
关键词：Image denoising; Non subsampled contourlet transform (NSCT); Support vector machine (SVM);Orthogonal matching pursuit.