Here is the code: import sys, math import numpy as np import matplotlib.pyplot as plt from skimage import io, color, filters, data from skimage.filters import threshold_otsu import matplotlib.image as mpimg from skimage.restoration import denoise_tv_chambolle, denoise_bilateral import skimage rawimg = open('1_A.png') img = mpimg.imread(rawimg) denoised_img = denoise_bilateral(img, sigma_range=0.1, sigma_spatial=15) #denoised_img2 = denoise_tv_chambolle(img, weight=0.1, multichannel=True) plt.imshow(denoised_img1) plt.show() On Thursday, July 30, 2015 at 4:23:34 PM UTC-7, Michael Alonge wrote:
Hello,
I have an image that I would like to do some smoothing over on.
Lets say there is a faint red spot on a white background. I would like to apply some algorithm that will smooth over the red with the white pixels surrounding the red pixels.
I thought this would be an application for the noise reduction tools ...
http://scikit-image.org/docs/dev/auto_examples/plot_denoise.html
... but the picture output looked the same as before.
What is the best algorithm for smoothing over pixels by re assigning a pixel value with the average value of the pixels surrounding it?
Thank you