Great thank you!
I have used some different filters and played around with some parameters. I also have performed a bunch of iterations of these filters to get more dramatic effects.
On Thursday, July 30, 2015 at 11:51:33 PM UTC-7, Emmanuelle Gouillart wrote:
did you try to change the value of the sigma_range parameter? 0.1 seems a bit low to me (ie, you only smooth a pixel with values very close to it). You can also try to change the value of the win_size parameter: increasing it will result in more smoothing.
Also the bilateral filter is a very good generic-purpose denoising filter, but if you want to start denoising with a function with less parameters, you can try the median filter skimage.filters.median
On Thu, Jul 30, 2015 at 04:26:55PM -0700, Michael Alonge wrote:
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,
On Thursday, July 30, 2015 at 4:23:34 PM UTC-7, Michael Alonge wrote:
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
apply some algorithm that will smooth over the red with the white
surrounding the red pixels.
I thought this would be an application for the noise reduction tools
... but the picture output looked the same as before.
What is the best algorithm for smoothing over pixels by re assigning
pixel value with the average value of the pixels surrounding it?