| 赵佰亭,董潇,贾晓芬.融合梯度及分数阶积分算子的SVM滤波[J].光电子激光,2019,30(7):750~758 |
| 融合梯度及分数阶积分算子的SVM滤波 |
| SVM filter method based on gradient and fractional integral operator |
| 投稿时间：2018-12-28 |
| DOI： |
| 中文关键词: SVM去噪 分数阶积分算子 PCNN 梯度 椒盐噪声 |
| 英文关键词:SVM denoising fractional integral operator PCNN image gradient salt and pepp er noise |
| 基金项目:国家自然科学基金(61501006)、中国博士后科学基金(2016M592035)、安徽省高等学校自然科学重点项目(KJ2017A076)、 安徽省高校优秀青年人才重点项目(gxyqZD2018036)和安徽省博士后科学基金(2017B173)资助项目 (安徽理工大学 电气与信息工程学院,安徽 淮南 232001) |
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| 中文摘要: |
| 为了在滤除椒盐噪声时更好的保护图像特征信息 ,利用分数阶积分算子、梯度信息和SVM 设计了一种滤波方法 FG-SVM。先设计PCNN噪点检测模型,将检测的噪点及信号点对应位置分别标记为1和0, 生成标记图像；然后根据标 记图像,在噪声图像上对每一个以信号点为中心的5×5区域,用中心 点周围的像素灰度信息、分数阶积分算子及梯度信 息构建训练样本,训练SVM获得去噪模型；再取以噪点为中心的5×5 区域构建测试样本,作为SVM去噪模型的输入来 估计区域中心的灰度值；最后用SVM的估计值取代噪点的灰度值,得到去噪图像。仿真试验 表明,分数阶积分阶次取 1.7时,能获得最好的去噪效果。对含噪 1%的Lena、Pepper及Camer.去噪,FG-SVM 的PSNR比MPCNN分别提高了[4.19,3.64]dB,且去噪图像的边缘细节清晰。 |
| 英文摘要: |
| To protect image features such as edge and texture information,an FG -SVM denoising method based on fractional integral operator and image gradient is designed.First,improve PCNN to enhance the accu racy of noise detection,and detect the position of noise pixels on the noise image,and generate mark image by marking the correspo nding position of noise pixels and signal pixels as 1and 0,respectively.Second,according to the mark image,in every 5×5region with signal pixel in the central,construct training samples using pixels grey information around the central pixel point,fractional -integral operators,and gradient information,then, train SVM with all training samples.Third,set up test sample in every 5×5reg ion with noise pixel in the central,and use it as the input of the trained SVM to estimate the pixel value of the central.Finally,th e estimate values of the SVM were used to replace noise pixel values,get the final denoised image.The simulation results show that it can obtain the best denoising effect when the fractional order is equal to 1.7±0.1.When denoising the images of Lena,Pepper and Camer. with noise concentration of 1%,the PSNR of FG-SVM is increased by [4.19,1.60,3.64] dB than MPCNN,FG-SVM produced visua lly pleasing denoising image with clear edge information. |
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