Opencv-图像去噪声

知识点

图像去噪声在OCR、机器人视觉与机器视觉领域应用开发中是重要的图像预处理手段之一，对图像二值化与二值分析很有帮助，OpenCV中常见的图像去噪声的方法有

• 均值去噪声
• 高斯模糊去噪声
• 非局部均值去噪声
• 双边滤波去噪声
• 形态学去噪声

这里暂时先说上面的三个方法，后面我们会在分享完相关知识点之后再来说。

python代码

import cv2 as cv
import numpy as np


def add_salt_pepper_noise(image):
    h, w = image.shape[:2]
    nums = 10000
    rows = np.random.randint(0, h, nums, dtype=np.int)
    cols = np.random.randint(0, w, nums, dtype=np.int)
    for i in range(nums):
        if i % 2 == 1:
            image[rows[i], cols[i]] = (255, 255, 255)
        else:
            image[rows[i], cols[i]] = (0, 0, 0)
    return image


def gaussian_noise(image):
    noise = np.zeros(image.shape, image.dtype)
    m = (15, 15, 15)
    s = (30, 30, 30)
    cv.randn(noise, m, s)
    dst = cv.add(image, noise)
    cv.imshow("gaussian noise", dst)
    return dst


src = cv.imread("C:/Users/qqxd/Desktop/opencvcode/images/example.png")
cv.imshow("input", src)
h, w = src.shape[:2]
src = gaussian_noise(src)
# cv.imshow("noise",src)
# src = add_salt_pepper_noise((src))

result1 = cv.blur(src, (5, 5))
cv.imshow("result-1", result1)

result2 = cv.GaussianBlur(src, (5, 5), 0)
cv.imshow("result-2", result2)

result3 = cv.medianBlur(src, 5)
cv.imshow("result-3", result3)

result4 = cv.fastNlMeansDenoisingColored(src, None, 15, 15, 10, 30)
cv.imshow("result-4", result4)

cv.waitKey(0)
cv.destroyAllWindows()

c++代码

#include 
#include 

using namespace cv;
using namespace std;

void add_salt_pepper_noise(Mat &image);
void gaussian_noise(Mat &image);
int main(int artc, char** argv) {
	Mat src = imread("C:/Users/qqxd/Desktop/opencvcode/images/example.png");
	if (src.empty()) {
		printf("could not load image...\n");
		return -1;
	}
	namedWindow("input", WINDOW_AUTOSIZE);
	imshow("input", src);
	gaussian_noise(src);

	Mat result1, result2, result3, result4;
	blur(src, result1, Size(5, 5));
	imshow("result-1", result1);

	GaussianBlur(src, result2, Size(5, 5), 0);
	imshow("result-2", result2);

	medianBlur(src, result3, 5);
	imshow("result-3", result3);

	fastNlMeansDenoisingColored(src, result4, 15, 15, 10, 30);
	imshow("result-4", result4);

	waitKey(0);
	return 0;
}

void add_salt_pepper_noise(Mat &image) {
	RNG rng(12345);
	int h = image.rows;
	int w = image.cols;
	int nums = 10000;
	for (int i = 0; i < nums; i++) {
		int x = rng.uniform(0, w);
		int y = rng.uniform(0, h);
		if (i % 2 == 1) {
			image.at<Vec3b>(y, x) = Vec3b(255, 255, 255);
		}
		else {
			image.at<Vec3b>(y, x) = Vec3b(0, 0, 0);
		}
	}
	imshow("salt pepper", image);
}

void gaussian_noise(Mat &image) {
	Mat noise = Mat::zeros(image.size(), image.type());
	randn(noise, (15, 15, 15), (30, 30, 30));
	Mat dst;
	add(image, noise, dst);
	imshow("gaussian noise", dst);
	dst.copyTo(image);
}

运行结果如下：

由此可见，不同的噪声类型需要去噪声的方法也是不一样的。