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作 者:甘建旺 沙芸[1] 张国英[2] GAN Jian-wang;SHA Yun;ZHANG Guo-ying(School of Information Engineering,Beijing Institute of Petrochemical Technology,Beijing 102617,China;School of Mechanical Electrical & Information Engineering,China University of Mining & Technology,Beijing 100083,China)
机构地区:[1]北京石油化工学院信息工程学院,北京102617 [2]中国矿业大学(北京)机电与信息工程学院,北京100083
出 处:《电子学报》2021年第6期1187-1194,共8页Acta Electronica Sinica
基 金:工业与信息化部2019年工业强基专项。
摘 要:曲率滤波算法通过构造滤波算子快速优化变分模型,但全变分曲率滤波及高斯曲率滤波易致去噪过平滑且椒盐噪声去除较差.提出了基于图像中值灰度相似度函数加权曲率滤波算法,其中,中值灰度相似度函数方差取决于小波变换最高频子带系数,能较好防止图像过平滑,且提高椒盐噪声去除能力;因此,采用中值灰度相似度函数分别对局部高斯曲率与局部全变分曲率投影算子加权,并分别迭代局部加权高斯曲率投影算子与局部加权全变分曲率投影算子,直至输出图像梯度总能量满足停止条件.实验表明,基于图像中值灰度相似度函数加权全变分曲率滤波与加权高斯曲率滤波比传统全变分曲率滤波和高斯曲率滤波去噪效果更好.The curvature filtering algorithm optimizes the variational model quickly by constructing a filter operator,but the total variational curvature filtering and Gaussian curvature filtering are easy to cause over smooth denoising with poor salt and pepper noise removal.A weighted curvature filtering algorithm based on image median gray similarity function is proposed,in which the variance of the median gray similarity function depends on the highest frequency subband coefficient of wavelet transform,which can prevent the image from being too smooth and improve the ability of removing salt and pepper noise.Therefore,the local Gaussian curvature projection operator and the local total variational curvature projection operator are weighted by the median gray level similarity function,and the local weighted Gaussian curvature projection operator and the local weighted total variational curvature projection operator are iterated respectively until the total energy of the output image gradient meets the stop condition.Experimental results show that the denoising effect of weighted total variation curvature filter and weighted Gaussian curvature filter based on image median gray similarity function is better than the traditional total variation curvature filter and Gaussian curvature filter.
关 键 词:加权全变分曲率滤波 加权高斯曲率滤波 中值灰度相似度函数 小波变换 椒盐噪声
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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