图像噪声滤波的两种支持向量机方法

Zekun Wang, Fuxi Zhang
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引用次数: 0

摘要

为了解决支持向量机基于预知的致命缺陷,提出了两种新的基于自学习机的支持向量机(SLM-SVM)图像噪声滤波方法,该方法比中值滤波器和自适应滤波器等非线性滤波器的滤波效果更好。说明了一系列的比较和工作参数,测试结果表明,这些方法可以处理完全未知图像中的噪声。并从图像中滤除99%以上的噪点。同时,由于这种滤波器的运行时间较长(超过2秒),并且在测试过程中出现了一些错误情况,因此还不成熟,提出了一些建议和预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two Support Vector Machine Methods for Image Noise Filter
To solve the fatal limitation of SVMs based on the pre-known, two new image noise filter methods Support Vector Machine based on self-learning machine (SLM-SVM) were presented, which is working better than many other non-linear filters, such as median filters and adaptive filters. A series of comparison and working parameters would be explained, performance of test showed these methods can dealing with the noise in a totally unknown image. And filter out more than 99% noise pixels from an image. In the same time, this kind of filter is still immature due to its long runtime (more than 2 second) and some error cases during testing, some recommendation and prediction were proposed.
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