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:

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