在不丢失 OpenCV 数据的情况下从图像中去除噪声

Question

我使用了这个代码：

    horizontalStructure = cv2.getStructuringElement(cv2.MORPH_RECT, (horizontalsize, 1))
    horizontal = cv2.erode(horizontal, horizontalStructure, (-1, -1))
    horizontal = cv2.dilate(horizontal, horizontalStructure, (-1, -1))

删除线条。

和一些过滤器来删除噪音和加粗字体：

 blur = cv2.GaussianBlur(img, (11, 11), 0)
 thresh = cv2.threshold(blur, 80, 255, cv2.THRESH_BINARY)[1]
 kernel = np.ones((2,1), np.uint8)
 dilation = cv2.erode(thresh, kernel, iterations=1)
 dilation = cv2.bitwise_not(dilation)

尽管有阈值和其他方法，如您所见，仍然存在很多噪声

这是我想要达到的结果：

您知道可以帮助我实现此结果的 OpenCV 过滤器吗？

Answer 1

以下解决方案并不完美，也不是通用的解决方案，但我希望它足以满足您的需求。

为了删除线，我建议使用 cv2.connectedComponentsWithStats 来查找集群，并屏蔽宽或长的集群。

该解决方案使用以下阶段：

将图像转换为灰度。
应用阈值并反转极性。
通过应用标志 cv2.THRESH_OTSU.
使用"close"形态学操作来缩小小间隙。
使用统计信息查找连接的组件（集群）。
迭代集群，删除宽度和高度大的集群。
删除非常小的簇 - 被认为是噪音。
顶部和左侧已清洁 "manually"。

代码如下：

import numpy as np
import cv2

img = cv2.imread('Heshbonit.jpg')  # Read input image

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert to Grayscale.

ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)  # Convert to binary and invert polarity

# Use "close" morphological operation to close small gaps
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.array([1, 1]));
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.array([1, 1]).T);

nlabel,labels,stats,centroids = cv2.connectedComponentsWithStats(thresh, connectivity=8)

thresh_size = 100

# Delete all lines by filling wide and long lines with zeros.
# Delete very small clusters (assumes to be noise).
for i in range(1, nlabel):
    #
    if (stats[i, cv2.CC_STAT_WIDTH] > thresh_size) or (stats[i, cv2.CC_STAT_HEIGHT] > thresh_size):
        thresh[labels == i] = 0
    if stats[i, cv2.CC_STAT_AREA] < 4:
        thresh[labels == i] = 0

# Clean left and top margins "manually":
thresh[:, 0:30] = 0
thresh[0:10, :] = 0

# Inverse polarity
thresh = 255 - thresh

# Write result to file
cv2.imwrite('thresh.png', thresh)

在不丢失 OpenCV 数据的情况下从图像中去除噪声

Remove noise from image without losing data in OpenCV

python

image

opencv

image-preprocessing