消除基于空间尺寸的图像噪点

Question

我想使用Python从RGB图像的主要特征周围消除噪点“五彩纸屑”。理想情况下，此过程将使示例图像中心的大特征（斑点）保持不变。即，仅当噪声的面积低于给定值时，才可以去除噪声吗？

我尝试在示例图像上使用OpenCV的fastNlMeansDenoisingColored函数（请参见下文），但这会从其余图像中去除明显的信号。

这是示例图片：

也可以是downloaded here。

import cv2
import matplotlib.pyplot as plt
import numpy as np

img = cv2.imread('example.png')

dst = cv2.fastNlMeansDenoisingColored(img,None,10,7,21)

# Original
plt.imshow(img)
plt.show()
print(np.nanmin(img),np.nanmax(img))
# denoised
plt.imshow(dst)
print(np.nanmin(dst),np.nanmax(dst))
plt.show()
# difference 
plt.imshow(img-dst)
plt.show()

Answer 1

如果只想要中央斑点，则可以选择查找轮廓并选择面积最大的轮廓。

代码：

#--- convert image to grayscale ---
imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)

#--- Perform Otsu threshold ---
ret2, th2 = cv2.threshold(imgray,0,255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('Threshold', th2)

它产生一个二进制图像：

#--- Finding contours using the binary image ---
 _, contours, hierarchy =    cv2.findContours(th2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

#--- finding the contour with the maximum area ---
big_contour = 0
max_area = 0

for cnt in contours:
    if (cv2.contourArea(cnt) > max_area):
        max_area = cv2.contourArea(cnt)
        big_contour = cnt

#--- creating a mask containing only the biggest contour ---
mask = np.zeros(imgray.shape)
cv2.drawContours(mask, [big_contour], 0, (255,255,255), -1)
cv2.imshow('Mask', mask)

#--- masking the image above with a copy of the original image ---
im2 = im.copy()
fin = cv2.bitwise_and(im2, im2, mask = mask.astype(np.uint8))
cv2.imshow('Final result', fin)