And third important type of noise will be a black and pepper.
Here we will due to a bit simplar visualization represent only a noise that has white pixels.
One approach to do so is to let's say simply take a "uniform_noise" image.
Set a threshold rule, where we will convert all pixels larger than a threshold to white (255) and we will set the remaining to zero.
impulse_noise = uniform_noise.copy()
Here a number 250 is defined as a threshold value.
Obviously, if we want to increase a number of white pixels we will need to decrease it.
Otherwise, we can increase it and in that way we will suppress the number of white pixels.
ret,impulse_noise = cv2.threshold(uniform_noise,250,255,cv2.THRESH_BINARY) cv2.imshow('Impuls noise',impulse_noise) cv2.waitKey() cv2.imwrite("Impuls noise.jpg",impulse_noise)