tutorial34_denoising_using_bilateral.py

#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

"""
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

img_gaussian_noise = cv2.imread('images/Osteosarcoma_01_25Sigma_noise.tif', 0)
img_salt_pepper_noise = cv2.imread('images/Osteosarcoma_01_8bit_salt_pepper.tif', 0)

img = img_gaussian_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()