直击面试——手写中值滤波过程

PS：中值滤波的概念和原理很多人都能用嘴说出来，但是有时候面试时就是需要手写中值滤波的操作，关键在面试时也没有那么多时间让你去考虑，所以在此记录一下。 

中值滤波概念就不具体展开了， 简单点就是利用一个 k*k 的滑窗，通过用这个小区域的中位数来代替产生滑窗的中心像素的像素值，

有关滤波、去噪的知识可以见：https://www.cnblogs.com/E-Dreamer-Blogs/p/10458846.html。

python实现

def Median_filtering(image,window_size):   #image为传入灰度图像，window_size为滤波窗口大小
    high, wide = image.shape img = image.copy() mid = (window_size-1) // 2 med_arry = [] for i in range(high-window_size): for j in range(wide-window_size): for m1 in range(window_size): for m2 in range(window_size): med_arry.append(int(image[i+m1,j+m2])) med_arry.sort() #对窗口像素点排序
            img[i+mid,j+mid] = med_arry[(len(med_arry)+1) // 2]     #将滤波窗口的中值赋给滤波窗口中间的像素点
            del med_arry[:] print("completed!") return img if __name__ == "__main__": img_path = "F:/python_code/test_make_other/2020-6-3-编写中值滤波" img_name = "./0.jpg" img = cv2.imread(img_name) img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY) new_img = Median_filtering(img,3) cv2.imwrite("Median_filtering.jpg",new_img)

C++ 实现

void medianFilter(unsigned char* Input_img, unsigned char* out_img, int width, int height) { memcpy(out_img, Input_img, width * height * sizeof(unsigned char)); for (int j = 1; j < height - 1; j++) { for (int i = 1; i < width - 1; i++) { int k = 0; unsigned char window[9]; // 3 * 3 的滑窗
                       for (int jj = j - 1; jj < j + 2; ++jj) for (int ii = i - 1; ii < i + 2; ++ii) window[k++] = Input_img[jj * width + ii]; // 将元素9个9个的放入到window中 // Order elements (only half of them) 排序
                       for (int m = 0; m < 5; ++m) // 选择排序
 { int min = m; for (int n = m + 1; n < 9; ++n) if (window[n] < window[min]) min = n; // Put found minimum element in its place 
 swap(window[m], window[min]); } out_img[j * width + i] = window[4];// window[4] 即为中位数
 } } }