【作者】 宁宁；

【导师】 付燕；

【作者基本信息】 西安科技大学 ， 计算机应用技术， 2011， 硕士

【摘要】 图像去噪是图像处理中关键的预处理环节。由于在含噪图像中,大部分噪声和图像细节分布在高频区域,不易区分,导致去噪时会不同程度地损坏图像的细节信息。因此,如何能在去噪的同时最大限度地保持细节信息是图像去噪研究的重点。针对在仅依赖单个图像特征时,基于支持向量机(Support Vector Machine, SVM)的图像去噪方法未能获得较好的去噪效果且会导致噪声点的识别率较低、分类器性能较差的问题,本文在分析总结图像去噪相关算法的基础上,探索了一种基于多特征结合与支持向量机的图像去噪方法。该方法根据图像中相邻像素的相关性及椒盐噪声的特点,利用多种特征相结合的方式来全面的描述像素点的属性,从而准确地区分噪声点和非噪声点。实验结果表明,基于多特征结合与支持向量机的图像去噪方法的去噪效果更优。此外,相对于基于单个特征的支持向量机分类器,基于多特征结合的支持向量机分类器对噪声点的识别率较高,且分类器性能更优。鉴于支持向量机集成(Support Vector Machine Ensemble, SVM Ensemble)的分类性能优于单个支持向量机,且稳定性、泛化能力更好,本文将支持向量机集成应用于图像去噪,提出了一种基于多特征结合与支持向量机集成的图像去噪方法。首先,根据图像中相邻像素的相关性及椒盐噪声的特点,提取含噪图像中的多种特征,并将其结合构成样本集;其次,对样本集进行归一化处理,并采用同时扰动训练样本和分类器模型参数的二重扰动机制及多数投票法构造支持向量机集成分类器;然后,利用支持向量机集成分类器识别含噪图像中的噪声点,再利用支持向量回归机对噪声点的灰度值进行回归预测;最后,重构图像达到去噪的目的。实验结果表明,该方法能进一步提高去噪效果,且在去噪的同时能较好地保持图像的细节信息,并在低噪声比下尤为有效。此外,与基于多特征结合的支持向量机分类器相比,基于多特征结合的支持向量机集成分类器具有更好的分类性能、稳定性和泛化能力。更多还原

【Abstract】 Image de-noising is a key pretreatment link before image is processed. For noisy image, most noise and image details are hard to be distinguished because they distribute in high frequency area, which leads to the loss of image detail information in different degree while noise points are removed. Therefore, how to retain image details to the utmost extent while noise points are removed is a research focus.Aiming at the problem that the approach for removing noise from images based on SVM fails to gain better de-noising effect and leads to low performance of classifier and noise recognition rate when only rely on a single image feature, an image de-noising method based on multi-feature combination and SVM is proposed on the basis of analyzing and summarizing the algorithms related to image de-noising. According to the adjacent pixels correlation in the image and the characteristics of salt-pepper noise, the method comprehensively describes the pixels by using the way of multi-feature combination. Thus noise points and non-noise points can be differentiated accurately. Experimental results show that the image de-noising method based on multi-feature combination and SVM gains better de-noising effect. In addition, the support vector classifier based on multi-feature combination has higher noise recognition rate and classification performance compared with support vector classifier based on single image feature.Considering that SVM ensemble has better classification performance, stability and generalization capability, SVM ensemble is applied to image de-noising. A new approach for removing salt-pepper noise based on multi-feature combination and SVM ensemble is presented in this thesis. First, according to the correlation of adjacent pixels in the image and the characteristics of salt-pepper noises, multiple features are extracted from noisy image and constituted sample set. Second, the sample set is normalized. Then the dual disturbance mechanism and majority voting method are applied to construct SVM ensemble classifier. The dual disturbance mechanism disturbs training set and classifier model parameter. Third, the SVM ensemble classifier is used to recognize noise points and support vector regression is applied to predicting the original gray value of noise points. Finally, image is reconstructed so as to achieve the purpose of de-noising. Experimental results show that this approach can further improve de-noising effect, and can retain detail information of image better while noise points are removed. The method is especially effective in lower noise ratio. Moreover, the SVM ensemble classifier based on multi-feature combination has better classification, stability and generalization capability compared with the SVM classifier based on multi-feature combination.更多还原