098_What is Bilateral denoising.py

#!/usr/bin/env python
__author__ = "Sreenivas Bhattiprolu"
__license__ = "Feel free to copy, I appreciate if you acknowledge Python for Microscopists"

# https://youtu.be/yenye2s90BA

"""
Spyder Editor

cv2.cv2.bilateralFilter - https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_filtering/py_filtering.html
skimage bilateral - https://scikit-image.org/docs/dev/auto_examples/filters/plot_denoise.html

https://people.csail.mit.edu/sparis/bf_course/course_notes.pdf

Bilateral is slow and not very efficient at salt and pepper
"""
import cv2
import numpy as np
from scipy.ndimage.filters import convolve
from skimage import io
from skimage.filters import median


img_gaussian_noise = cv2.imread('images/BSE_25sigma_noisy.jpg', 0)
img_salt_pepper_noise = cv2.imread('images/BSE_salt_pepper.jpg', 0)

img = img_salt_pepper_noise

bilateral_using_cv2 = cv2.bilateralFilter(img, 5, 20, 100, borderType=cv2.BORDER_CONSTANT)

#d - diameter of each pixel neighborhood used during filtering
# sigmaCOlor - Sigma of grey/color space. 
#sigmaSpace - Large value means farther pixels influence each other (as long as the colors are close enough)


from skimage.restoration import denoise_bilateral
bilateral_using_skimage = denoise_bilateral(img, sigma_color=0.05, sigma_spatial=15,
                multichannel=False)

#sigma_color = float - Sigma for grey or color value. 
#For large sigma_color values the filter becomes closer to gaussian blur.
#sigma_spatial: float. Standard ev. for range distance. Increasing this smooths larger features.




cv2.imshow("Original", img)
cv2.imshow("cv2 bilateral", bilateral_using_cv2)
cv2.imshow("Using skimage bilateral", bilateral_using_skimage)

cv2.waitKey(0)          
cv2.destroyAllWindows()