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Several Filtering Algorithms for Removing Mixed Noises in Digital Image

Author: LuoXiaoJun
Tutor: LiBing
School: Changsha University of Science and Technology
Course: Applied Mathematics
Keywords: Digital image Mixed noise Filtering algorithm Gradient operator
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 164
Quote: 5
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Abstract


The image often will be in the process of generating and transmitting a variety of noise pollution, resulting in a decline in the quality of the image. In the application, there are two types of noise models can be fully representative of the majority of the image noise, the Gaussian noise and impulse noise, the the two noise mixed noise in practice is difficult to avoid, the removal of this mixed noise is image processing in an important and challenging issue. In recent years, new work in mixed noise removal continue, for example, off the new equality in 2005 image denoising the mixing noise filtering algorithm; the Washington University R.Garnett et al in 2005 proposed the Tr the? ilateral the Filter; Liu, Quansheng and Bing in 2008 the mixed noise nonlocal filtering algorithm (MNF). For Gaussian noise and consistent pulse noise mixed noise filtering on the basis of the above work, mainly to study two types of algorithms: First, on the basis of the Trilateral Filter with \\Then on the basis of the LMF two exploration instead of the the ROAD statistic detection pulse noise: use ROLD Statistics, a of mixed noise linear filtering based on ROLD statistics the algorithm (ROLD-LMF), referred to as RLMF The algorithm is improved to some extent, consistent impulse noise removal capacity, as well as the effect of the removal of mixed noise. Further, the application of gradient can enhance the characteristics of the image detail and the boundary, in the weight function of similarity instead of the gray value of the neighborhood pixels gradient similarity, proposed based on the information similar to the pixel structure of the mixed noise linear filtering algorithm (Gradient-LMF), referred to as GLMF, effective protection of the image detail and the boundary to a certain extent. Is discussed on the basis of the MNF MNF accelerated algorithm, two algorithms: First, the use of two similar windows in the ratio of the mean and variance ratio as a threshold acceleration algorithm based on pixel similarity MNF (Fast - MNF), referred FMNF, the operation speed of the algorithm about 10% higher than the MNF, and the denoising effect is not less than the MNF. The second is the use of image texture detail with directional characteristics, acceleration algorithm based on directional texture detail the MNF (Direct-MNF), referred to DMNF. The computing speed of the algorithm denoising effect is not lower than the MNF increased more than 25%. The main content of this paper is divided into six chapters. First chapter introduces the application of digital image processing and image denoising significance, image denoising Research; Chapter II introduces digital image noise, the theoretical basis of image denoising; Chapter III describes the last decade and this article several algorithms; Chapter proposed a new class of hybrid noise linear filtering algorithm; the Chapter put forward the the two MNF filtering speed: FMNF and the DMNF; sixth chapter summarize the work of this paper, and in the future direction of research.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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