Python | Denoising of colored images using opencv (original) (raw)
Last Updated : 04 Jan, 2023
Denoising of an image refers to the process of reconstruction of a signal from noisy images. Denoising is done to remove unwanted noise from image to analyze it in better form. It refers to one of the major pre-processing steps. There are four functions in opencv which is used for denoising of different images.
Syntax: cv2.fastNlMeansDenoisingColored( P1, P2, float P3, float P4, int P5, int P6)
Parameters:
P1 – Source Image Array
P2 – Destination Image Array
P3 – Size in pixels of the template patch that is used to compute weights.
P4 – Size in pixels of the window that is used to compute a weighted average for the given pixel.
P5 – Parameter regulating filter strength for luminance component.
P6 – Same as above but for color components // Not used in a grayscale image.
Below is the implementation:
import
numpy as np
import
cv2
from
matplotlib
import
pyplot as plt
img
=
cv2.imread(
'bear.png'
)
dst
=
cv2.fastNlMeansDenoisingColored(img,
None
,
10
,
10
,
7
,
15
)
plt.subplot(
121
), plt.imshow(img)
plt.subplot(
122
), plt.imshow(dst)
plt.show()
Output: