a morphological approach to remove salt and pepper noise in images

Aravinth Chinnapalanichamy et al ,Int.J.Computer Technology & Applications,Vol 3 (6), 1875-1880
ISSN:2229-6093
The steps involved in phase 1 are specified as follows:
1. The processing pixel f(x, y) is extracted along with
its neighbourhood pixels. The mask size is chosen
according to the noise density
noise pixels occupying a very less area of the bell shaped
curve of normal distribution (i.e.) the noise occupies only both
the extremes and the noise free pixels are centred on the mean
value of the neighbourhood.
MASK SIZE NOISE
DENSITY
3 X 3 0-30%
5 X 5 30-60%
7 X 7 60- 90%
For increasing noise density, the mask size should
also be linearly increased for better noise
identification. However increasing the mask size
also imposes a blurry effect on the restored image.
G = f x + s, y + t were s, t = −k to k
and (2k + 1 X 2k + 1) is te mask size
1. The proposed scheme is applied for an 8 bit image
(Intensity range-0 to 255). The centre pixel is
verified for 0 or 255. If it is one of the two values,
then it is retained as a possible candidate for noise.
Or else it is considered as noise free and the next
pixel along with its neighbourhood is taken for
processing.
N(x, y) =
1, f x, y = 0 or 255
0, else
2. If N(x, y) =1, then the matrix G is checked for
values 0 and 255. If all the values in G are 0 and
255, then sub stage 1 is used and if there are
different intensity levels (0 to 255), then sub stage 2
is selected for noise identification.
G =
substage 1, G(G ≠ 0 |255 = 0
substage 2, else
3.1 Sub stage 1- Image restoration in white
and black regions
The window extracted from a white or black region
from a noise free image will have all its values as either 0 or
255 .However if impulse noise is present, depending on noise
density level, noisy 0 values replace some of the 255 values
in white region and vice versa. But the majority of the pixels
in a chosen window will be either 0 or 255 based on the
region. Hence the no of 0’s and 255’s in the window is
counted and f(x, y) is replaced with the value occurring
maximum no of times.
f x, y =
0, Na > Nb
255, else
were Na = no of 0 ′ s in G and Nb = no of 255 ′ s inG.
3.2 Sub stage 2- Noise Identification in
Gray shade regions
The distribution of pixels in a particular
neighbourhood follows nearly a normal distribution with the
68% of the total area of the curve is always
considered as noise for processing according to the algorithm
although it may also contain some noisy pixels for higher
noise densities. As proposed earlier, pixel connectivity is used
to identify a noisy pixel. Hence connectivity is established
between the pixels contained within the remaining 32% of the
curve. This portion of the curve may also contain some noise
free pixels. However, this does not cause significant problem
for noise identification.
For establishing connectivity, certain conditions are
specified based on which the intensity values in the extracted
window are changed to either 0 (background pixel) or
1(foreground pixel) and a 4-connectivity is established
between the foreground pixels (1). The connection thus
established between the foreground pixels is actually a
connection among the noisy pixels in the chosen window.
This procedure is given in the following steps.
1. A function H is found which is a subset of G with G
(centre) removed. Then the mean (µ) and standard
deviation (σ) of H is found. From that, two values (R1 and
R2) are found.
R1=µ-σ and R2=µ+σ
It is seen that almost all the noise free pixels are
contained within this range and the noisy values are located
outside the range. However there are certain cases like images
with high noise density or regions with intensity values closer
to 0 & 255 where the noisy pixels are also included in the
range because the mean will be centred on 0 or 255 in either
case.
2. Now the values of H are converted to any of the three
values 0, 255 and constant K based on the following
conditions. This is based on the assumption that the values
contained within the range R are noise free and those
outside the range are noise.
IJCTA | Nov-Dec 2012
Available online@www.ijcta.com
1876