Try gaussian smoothing across different scenarios

gauss_smooth.py

import cv2
import matplotlib.pyplot as plt


kernel = (7, 7)
imgs = {
    "salt_n_pepper": cv2.imread("Noise_salt_and_pepper.png"),  # thank you wikipedia
    "wrinkles": cv2.imread("old_man.jpeg"),  # images.pexels.com
    "tripod": cv2.imread("tripod.png") # classic b/w man looking at camera on tripod
}

'''
The thing is, gaussian smoothing cannot handle everything
'''
imgs["gauss_blur"] = cv2.GaussianBlur(imgs["salt_n_pepper"], kernel, 1)
imgs["median_blur"] = cv2.medianBlur(imgs["salt_n_pepper"], kernel[0])

'''
And gaussian can ruin our sharp edges for edge detection
'''
imgs["gauss_edge"] = cv2.GaussianBlur(imgs["wrinkles"], kernel, 1)
imgs["bilat_edge"] = cv2.bilateralFilter(imgs["wrinkles"], 5, 10, 10)
imgs["gauss_canny"] = cv2.Canny(imgs["gauss_edge"], 0, 255)
imgs["bilat_canny"] = cv2.Canny(imgs["bilat_edge"], 0, 255)

'''
We can remove low frequency, gaussian pretty well though and avoids the inconsistency of the box filter for example
because of it's smooth freq domain shape.
'''
kernel = (17, 17)
imgs["gauss_tripod"] = cv2.GaussianBlur(imgs["tripod"], kernel, 1)
imgs["box_tripod"] = cv2.boxFilter(imgs["tripod"], -1, kernel)

for (k, v) in imgs.items():
    cv2.imshow(k, v)

'''
Along the way, I came across: http://docs.opencv.org/3.1.0/d4/d13/tutorial_py_filtering.html
'''
plt.plot([0], [0])
plt.show(block=True)