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机构地区:[1]电子科技大学计算机科学与工程学院,成都611731 [2]数字媒体技术四川省重点实验室,成都611731
出 处:《计算机应用》2016年第10期2826-2831,共6页journal of Computer Applications
基 金:国家自然科学基金资助项目(61202255);教育部广东省产学研重大专项(2012A090300001)~~
摘 要:针对排序统计类降噪算法在随机脉冲噪声(RVIN)图像降噪过程中,对图像边缘和细节部分噪声识别不够准确以及恢复比较模糊的问题,提出了基于加权空间离群点度量(SLOM)的脉冲噪声降噪算法WSLOM-EPR。该算法以优化的空间距离差为基础,引入图像邻域均值和标准差,建立反映局部边缘细节特征的噪声检测方法,提高边缘细节处噪声的识别精度;然后以精确检测结果为基础,优化保边正则(EPR)函数,提高算法的执行效率,并增强算法保留边缘细节的能力。仿真结果显示,WSLOM-EPR算法在40%到60%噪声密度下对噪声点的误检和漏检综合表现优于对比算法,且能在两者之间保持一个较好的平衡;降噪后的峰值信噪比(PSNR)好于对比算法中的大多数情况,且边缘细节在视觉上更加清晰连续。结果表明WSLOM-EPR算法提高了噪声检测精度,有效地保持了恢复图像的边缘细节信息。In order to alleviate the problem of inaccurate noise identifying and blurred restoration in image edges and details, a novel algorithm based on weighted Spatial Local Outlier Measure (SLOM) was proposed for removing random-valued impulse noise, namely WSLOM-EPR. Based on optimized spatial distance difference, the mean and standard deviation of neighborhood were introduced to set up a noise detection method for reflecting local characters in image edges, which could improve the precision of noise identification in edges. According to the precision detection results, the Edge-Preserving Regularization (EPR) function was optimized to improve the computation efficiency and preserving capability of edges and details. The simulation results showed that, with 40% to 60% noisy level, the overall performance in noise points detection was better than that of the contrast detection algorithms, which can maintain a good balance in false detection and miss detection of noise. The Peak Signal-to-Noise Ratios (PSNR) of WSLOM-EPR was better than that of the most of the contrast algorithms, and the restoring image had clear and continuous edges. Experimental results show that WSLOM-EPR can improve detection precision and preserve more edges and details information.
关 键 词:随机脉冲噪声 空间离群点度量 局部统计 保边正则 降噪
分 类 号:TP391.41[自动化与计算机技术—计算机应用技术]
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