混合中值滤波器 ( Hybrid Median Filter ) C++ 实现_app

概述在中值滤波器和均值滤波器之后, 我们看到是中值滤波器的改版, 这个名字我实在不好把握 Hybrid Median Filter实在不知道翻译成什么比较妥当.只好沿用了混合动力车里面的混合二字, 姑且把这种滤波器命名为混合中值滤波器. 这个滤波器的工作流程较之原先有些繁琐, 来看看这张图图截的有些模糊, 但是还是看出了大致的流程, 首先当前像素的上下左右和自身取中值 , 然后左上右上左下右下和自身

在中值滤波器和均值滤波器之后,我们看到是中值滤波器的改版,这个名字我实在不好把握 HybrID Median Filter实在不知道翻译成什么比较妥当.只好沿用了混合动力车里面的混合二字,姑且把这种滤波器命名为混合中值滤波器. 这个滤波器的工作流程较之原先有些繁琐,来看看这张图

图截的有些模糊,但是还是看出了大致的流程,首先当前像素的上下左右和自身取中值 ,然后左上右上左下右下和自身取中值 ,完了前面的两个值和当前像素值再取一次中值 ,得到的值就是最后的终极像素值了. 可以看到这个流程还是有些复杂的.. 那他的效果如何捏? 我们还是和前几次一样来观察一把:

原图 HMF之后:

噪声污染: HMF之后:

从前面的图片可以看到,图片从高锐度经由混合中值滤波之后,锐度下降了. 但是在去噪声的环节上,这个所谓的混合中值滤波器表现的甚至不如中值滤波来的效果好,当然因为这里的噪声是随机的,可能有些因素在其中. 但从原理上分析.这种滤波器确实存在不能过滤掉的可能,原因在什么地方呢? 且看我们这个大图中的一个小局部:

把图放大之后看的很清楚了,在红色的圆圈圈出来的地方,不出意外的发现了水平连续的3个白色像素. 再联想到我们之前经过HMF之后的效果图就不难得出结论,就是者连续的3个点造成了最后的这个白色像素. 怎么说呢? 很简单,在HMF的第一步过程中,因为我们选择的点是5各点,上下左右中,那么经过计算,就是白色无疑了,不管第二步得到什么像素,在第三步的时候,让2个白色取中值,肯定得到的白色像素了! 仔细想来确实是这个么道理. 但是median filter的时候因为要整体考虑9个像素的中值,这里很有可能就不是白色了! 所以这个混合中值滤波器会在保持线条的边缘上有独到之处(因为比较容易保持联系像素的颜色不受伤害),而消除噪声还是median filter表现更胜一筹.

老规矩,最后再贴一下处理的函数代码:

unsigned char median(unsigned char* elements,int wIDth){ // Order elements (only half of them) for (int i = 0; i < (wIDth >> 1) + 1; ++i) { // Find position of minimum element int min = i; for (int j = i + 1; j < wIDth; ++j) if (elements[j] < elements[min]) min = j; // Put found minimum element in its place unsigned char temp = elements[i]; elements[i] = elements[min]; elements[min] = temp; } // Get result - the mIDdle element return elements[wIDth >> 1];}/**** method to remove noise from the corrupted image by hybrID median value* @param corrupted input grayscale binary array with corrupted info* @param smooth output data for smooth result,the memory need to be allocated outsIDe of the function* @param wIDth wIDth of the input grayscale image* @param height height of the input grayscale image*/voID hybrIDMedianFilter (unsigned char* corrupted,unsigned char* smooth,int wIDth,int height){ memcpy ( smooth,corrupted,wIDth*height*sizeof(unsigned char) ); for (int j=1;j<height-1;j++) { for (int i=1;i<wIDth-1;i++) { unsigned char window[5]; unsigned char results[3]; // Pick up cross-window elements window[0] = corrupted[(j - 1) * wIDth + i]; window[1] = corrupted[j * wIDth + i - 1]; window[2] = corrupted[j * wIDth + i]; window[3] = corrupted[j * wIDth + i + 1]; window[4] = corrupted[(j + 1) * wIDth + i]; // Get median results[0] = median(window,5); // Pick up x-window elements window[0] = corrupted[(j - 1) * wIDth + i - 1]; window[1] = corrupted[(j - 1) * wIDth + i + 1]; window[2] = corrupted[j * wIDth + i]; window[3] = corrupted[(j + 1) * wIDth + i - 1]; window[4] = corrupted[(j + 1) * wIDth + i + 1]; // Get median results[1] = median(window,5); // Pick up leading element results[2] = corrupted[j * wIDth + i]; // Get result smooth[j*wIDth+i] = median(results,3); } }}@H_301_60@</p> <p>因为要取中值,还附加了一个取中值的函数median:)</p>  总结 <p>以上是内存溢出为你收集整理的混合中值滤波器 ( Hybrid Median Filter ) C++ 实现全部内容，希望文章能够帮你解决混合中值滤波器 ( Hybrid Median Filter ) C++ 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