Procedure:
1. Add noise to the original image:
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I applied the following noises using genradn and imnoise in Scilab and obtained their corresponding PDF p(z):
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2. Filter the noisy images
I made and then applied the following filters on each type of noise in (1).
Assuming a rectangular sub-image with of size n*m, n=m=3( for nearest neighbor perimeter)
1.ARITHEMETIC FILTER
2.GEOMETRIC FILTER
3.HARMONIC FILTER
4.CONTRAHARMNIC FILTER
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3. Determined which type of filter suited for each type noise.
In the following images the arrangement is as follows:
from the top- left-right :
(1) NOISY image (2) Filtered Image with Arithmetic Filter, (3) Arithmetic Filter
bottom-left-right is :
(4) Harmonic Filter, (5) Contraharmonic Filter : Q=-1, (6) Contraharmonic Filter : Q=+1.
From these results, all the filter worked but I think the contraharmonic filter with positive Q =+1 was most effective in removing the uniform noise since the filter at Q=+1 is beginning to restore the white circle at the center.
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4.GAMMA NOISE AND FILTERING
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5.UNIFORM NOISE , PDF AND FILTERING
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6.SALT AND PEPPER NOISE AND FILTERING
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7.SPECKLE NOISE AND FILTERING
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SOURCE: A18 – Noise models and basic image restoration manual
GRATITUDE: I would like to thank Mandee and Alva for discussing with me :) (salamat) and Gibert for the rayleigh.