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, multichannel=True)
> 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 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 ...
> ... 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