融合方向特性与不确定性的脉冲噪声滤波

发布时间：2018-05-28 06:03

  本文选题：随机脉冲噪声 + 阈值敏感性　； 参考：《中国图象图形学报》2017年06期

【摘要】：目的随机噪声的噪声阈值具有不确定性和敏感性,寻找一个鲁棒的阀值是非常困难的,这严重影响了噪声的提取效率。为提高噪声判断的准确性,提出一种基于方向特性与中智不确定性融合的双端脉冲检测算法;另外,为加强优良像素在滤波过程中的权重,构建了一种基于像素中智不确定性和ROAD(rank-ordered absolute differences)统计量的新型双边滤波函数。方法在噪声检测阶段,首先根据ROLD(rank-ordered logarithmic difference)与噪声阈值T的关系,将污染图像的像素分为超限域像素(ROLD≥T)、邻限域像素(0.8T≤ROLDT)和安全域像素(ROLD0.8T),并利用开关机制完成一次噪声检测。在此基础上,为提高超限域和邻限域像素噪声检测的准确性,采用不同策略对其进行二次噪声排查:对超限域像素,利用新型25像素和9像素4方向模板计算像素基于排序的方向对数差统计量,由该统计量与T的大小关系决定当前像素的噪声真伪;对邻限域像素,则结合当前像素中智不确定性在滤波窗内的排序信息来进一步确定其噪声特性。在滤波阶段,利用像素中智不确定性和ROAD统计量构建新型双边滤波函数,以加强低不确定性和高相似性像素在图像恢复中的权重。结果针对实验图像,双端脉冲检测算法的边缘像素提取率最高可达67%、邻限域像素的噪声剔除率最高可达91%,大大降低了阈值对噪声提取的敏感性,从而提高了噪声判断的正确率。在10%~80%噪声范围内,本文算法的主观性能和峰值信噪比都优于其他7种算法。结论本文基于双端检测和新型双边滤波函数的新算法,在噪声检测和去噪过程中均充分考虑了图像本身的方向性和噪声的不确定性,因此提高了噪声提取及像素滤波权重的准确性,从而有效地保护了图像的边缘和细节信息。
[Abstract]:Objective the noise threshold of random noise is uncertain and sensitive, so it is very difficult to find a robust threshold, which seriously affects the efficiency of noise extraction. In order to improve the accuracy of noise judgment, a two-terminal pulse detection algorithm based on the fusion of directionality and uncertainty is proposed, and the weight of fine pixels in the filtering process is enhanced. A new two-sided filter function based on pixel intelligence uncertainty and ROAD(rank-ordered absolute differences statistics is constructed. Methods in the phase of noise detection, according to the relationship between ROLD(rank-ordered logarithmic difference) and noise threshold T, the pixels of contaminated image were divided into two categories: the pixel in the over-limited domain (r), the pixel in the adjacent domain (0.8T 鈮，

本文编号：1945590