Image Processing



  Basics   Image Operations
  Basic Operations   Erosion and Dilation
  OpenCV - Gaussian Blur
  OpevCV-Edge Detection
  Histogram Equalization   Histogram Plot
  Denoising Image
  BGR color palette
  Grabcut Algorithm
  Image Extraction   Video Reversing


Denoising Image

Denoising:

Denoising of an image refers to the process of reconstruction of a signal from noisy images. Denoising is done to remove unwanted noise from images 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.
# 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('abc.jpg')

# 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()

cv2.waitKey(0)
cv2.destroyAllWindows()

 

Output: