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: Python

# importing libraries
import numpy as np
import cv2
from matplotlib import pyplot as plt

# Reading image from folder where it is stored
img = cv2.imread('bear.png')

# denoising of image saving it into dst image
dst = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 15)

# Plotting of source and destination image
plt.subplot(121), plt.imshow(img)
plt.subplot(122), plt.imshow(dst)

plt.show()

Output: