a morphological approach to remove salt and pepper noise in images
a morphological approach to remove salt and pepper noise in images
a morphological approach to remove salt and pepper noise in images
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Arav<strong>in</strong>th Ch<strong>in</strong>napalanichamy et al ,Int.J.Computer Technology & Applications,Vol 3 (6), 1875-1880<br />
ISSN:2229-6093<br />
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If f(x, y) =0, first all H values equal <strong>to</strong> zero are<br />
changed <strong>to</strong> 1 <strong>and</strong> then all the other values are<br />
changed <strong>to</strong> 0. The order <strong>in</strong> which the values are<br />
changed is important <strong>in</strong> this case.<br />
If f(x, y) =255, then all the H values equal <strong>to</strong> 255<br />
are changed <strong>to</strong> 1 <strong>and</strong> all the other values are<br />
changed <strong>to</strong> 0.<br />
3. All the above conditions <strong>and</strong> change of values are done<br />
only <strong>to</strong> facilitate f<strong>in</strong>d<strong>in</strong>g the connectivity between<br />
pixels. The 4- connectivity between foreground pixels<br />
(value 1) <strong>in</strong> matrix H is found (i.e.) no of adjacent<br />
pixels (N) connected <strong>to</strong> the centre pixel is counted.<br />
4. If this number (N) is greater than a particular value,<br />
say A, then the process<strong>in</strong>g pixel is said <strong>to</strong> be <strong>noise</strong> free.<br />
If it is less than A, then it is <strong>noise</strong>. The value A<br />
depends on the mask size. For a mask of size( 2k+1 x<br />
2k+1 ), the m<strong>in</strong>imum no of pixels that should be<br />
connected <strong>to</strong>gether so that the centre pixel is <strong>noise</strong> free<br />
is (k+1)*(2k+1)<br />
f(x, y) = <strong>noise</strong>free, N > A<br />
<strong>noise</strong>, N ≤ A<br />
were N is te maximum no of adjacent pixels connected<br />
<strong>to</strong> centre pixel based on 4 − connectivity <strong>and</strong> A =<br />
k + 1 ∗ (2k + 1)<br />
the median of those values are calculated <strong>and</strong> that median<br />
value replaces f(x, y). The steps of phase 2 is shown <strong>in</strong> Fig.2<br />
1. For a mask of size (2k+1 x 2k+1), the follow<strong>in</strong>g values<br />
are extracted.<br />
f ′ = f(x + s, y + t)<br />
were s, t = 0 <strong>to</strong> 1<br />
2. The values other than 0 <strong>and</strong> 255 are found <strong>in</strong> f’. Then<br />
the median of those values is calculated which <strong>in</strong> turn is<br />
the estimated correct value.<br />
f x, y = med(f ′ f ′ ≠ 0 255<br />
3. If all the values of f’ are either 0 or 255, then the upper<br />
limit of s<strong>and</strong> t is <strong>in</strong>cremented by 1. If still the function<br />
f’ has no values other than 0 <strong>and</strong> 255, then the limit is<br />
aga<strong>in</strong> <strong>in</strong>cremented by 1 <strong>and</strong> this <strong>in</strong>crement happens till<br />
K.<br />
f ′ = f(x + s, y + t)<br />
were s, t = 0 <strong>to</strong> p, p = 0,1, … k<br />
And f x, y = med(f ′ f ′ ≠ 0 255 .<br />
4. PHASE 2- NOISE REMOVAL USING<br />
MEDIAN FILTER OF VARYING MASK<br />
SIZE<br />
Once the pixel has been identified as <strong>noise</strong>, then it is<br />
replaced with an estimated value of the correct <strong>in</strong>tensity level.<br />
From the G matrix, certa<strong>in</strong> elements are extracted <strong>and</strong> then<br />
5. EXPERIMENTAL RESULTS<br />
Simulations are done on various st<strong>and</strong>ard <strong>images</strong> at<br />
different <strong>noise</strong> levels. The performance of this scheme is<br />
measured by us<strong>in</strong>g the parameter PSNR <strong>and</strong> compared with<br />
the PSNR values of other methods <strong>and</strong> is shown that our<br />
method is superior <strong>to</strong> other methods.<br />
The st<strong>and</strong>ard <strong>images</strong> chosen for test<strong>in</strong>g are Lena, Boat <strong>and</strong><br />
gold hill. All the <strong>images</strong> are 8 bit gray scale <strong>images</strong> with size<br />
512x512. The mask size is chosen accord<strong>in</strong>gly <strong>to</strong> the <strong>noise</strong><br />
density level. For <strong>in</strong>stance, while test<strong>in</strong>g with Lena image<br />
corrupted by impulse <strong>noise</strong> level of 70%, the mask size<br />
chosen is 5x5. This implies that the value of K is 2. Further<br />
while identify<strong>in</strong>g a noisy pixel <strong>in</strong> sub stage 2, the value of A is<br />
set as 15. While process<strong>in</strong>g a particular pixel of the image, if<br />
IJCTA | Nov-Dec 2012<br />
Available onl<strong>in</strong>e@www.ijcta.com<br />
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