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PSGCAS Search: A Journal of Science and Technology Volume : 4 No. : 1, ISSN: 2349 – 5456 46
A Comparative Study about Various Noise Removal Filters and Edge
Detection in MATLAB
Subhashini. A
Department of Computer science, PSG College of Arts & Science, Coimbatore, Tamil Nadu,
India
*Corresponding Author: [email protected]
ABSTRACT
MATLAB (Matrix laboratory) is used to perform various digital image processing functions in an
image. In this paper, MATLAB is used for implementation of various filters and edge commands. It is
also used to calculate elapsed time of various filter and edge commands in MATLAB. Some of the
implemented filters are speckles, pepper and noise, Gaussian filters etc. Aim of this paper is to include
various filters with its coding for output comparisons. At the same time, we can include noise to the
images. Finally, comparison of time taken for each command will be calculated and compared.
Keywords: MATLAB, filters, edge commands, Edge detection
INTRODUCTION
Digital image processing is used to
perform operations on a digital image1. It
is used for classification, feature
extraction, pattern recognition etc.
METHODOLOGY
In this work, 3 steps are included
for converting RGB image to gray image
and converting actual image to blurred
image etc as step-1.
a. Methods Used
1. Conversion of RGB to gray
2. Actual image to blurred image
Basically there are three types of noises
available in digital image processing. To
include noise into a picture, we have used
Gaussian, salt and pepper and speckle
noise to the image with its coding2. Then
to remove these noises from the image, we
have included filters as step-2.
b. Filters used
• Motion blurred filters
• Median
• Wiener
• Gaussian
• Average
• Log
• Unsharp
c. Edge commands used
• Prewitt Edge
• Sobel edge
d. Edge detection
Several methods are used for edge
detection in an image. Some of the
methods used here are (Step 3):
• Canny edge
• Roberts edge
Methods with Codings
img=imread('e:\images\flower\flowers-
46.jpg');
i=rgb2gray(img);
figure,imshow(img);
title ('Actual image');
Subhashini A.
PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 1, ISSN: 2349 – 5456 47
blur=imfilter(img,h,'replicate');
figure,imshow(blur);
title('blurred image');
t=fspecial('unsharp') 3
;
shar=imfilter(img,t,'replicate');
figure,imshow(shar);
title ('sharpen image');
l=imnoise(img,'speckle',0.02);
figure,imshow(l);
title('Speckle Noise image');
l=imnoise (img,'Gaussian',0.02);
figure,imshow(l) 4
;
title ('Gaussian image');
Actual image
blurred image
sharpen image
Speckle Noise image
A Comparative Study on Various Noise Removal Filters and Edge Detection in MATLAB
PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 1, ISSN: 2349 – 5456 48
l=imnoise(i,'salt & pepper',0.02);
figure,imshow(l);
title('Salt & pepper Noise image');
k = medfilt2(l,[3 3]);
figure,imshow(k);
title('MedianFilter')5;
j = wiener2(i, [3 3]);
figure,imshow(j);
title('Wiener Filter');
bw = edge(i);
figure,imshow(bw);
title('Edge command');
BW = edge(i,'sobel');
figure,imshow(BW);
title('Sobel Edge command') 6
;
BW = edge(i,'prewitt');
figure,imshow(BW);
title('PrewittEdge command');
Gaussian image
Salt & pepper Noise image
Median Filter
Wiener Filter
Edge command
Sobel Edge command
Subhashini A.
PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 1, ISSN: 2349 – 5456 49
BW = edge(i,'roberts');
figure,imshow(BW);
title('Roberts Edge command');
BW = edge(i,'canny');
figure,imshow(BW);
title('Canny Edge command');
BW = edge(i,'log');
figure,imshow(BW);
title('Log Edge command');
%sliding-neighborhood operations
using nlfilter
fun = @(x) median(x(:));
b = nlfilter(img,[3 3],fun);
figure,imshow(b);title('NLFilter');
Prewitt Edge command
Roberts Edge command
Canny Edge command
Log Edge command
A Comparative Study on Various Noise Removal Filters and Edge Detection in MATLAB
PSGCAS Search: A Journal of Science and Technology Volume: 4 No. : 1, ISSN: 2349 – 5456 50
Elapsed time calculation
The following table contains the time
taken for each command when executed in
MATLAB7. This will help the researchers
to make their decisions. Edge detection is
used for image segmentation and data
extraction in areas such as image
processing, computer vision, and machine
vision.
Table 1
Elapsed time for the commands used
Noise Removal filters
Command
Name
Elapsed time (in
seconds)
Imfilter 0.055884
Fspecial 0.037882
Speckle 0.479691
Gaussian 0.467460
Salt & pepper 0.213972
Median 0.452338
Wiener 0.741304
Edge commands
Edge 18.487140
Sobel 18.632645
Prewitt 18.958424
Roberts 19.179785
Canny 2.951014
Log 0.739765
Elapsed time for all commands was
executed with the help of tic and toc
commands in MATLAB8. After applying
all the commands, the elapsed time for the
commands were measured (Table 1).
Among all the noise removal filters
salt and pepper command executes very
fast and wiener filters took large amount of
time for execution. In edge commands, log
edge command took small amount of time
whereas Roberts edge detection command
took maximum amount of time. The spinor
Fourier transform splits into two usual
complexes Fourier transforms for edge
detection9.
CONCLUSION
In the present investigation so
many filter commands were exposed with
their output. This work is going to be
useful for those who want to remove noise
from images10
. Different noises are
removed by using different filters such as
Wiener filter, Gaussian filter, nlfilter etc at
different levels. Getting clear images is the
aim of pre-processing process in image
processing. For any image the edges are
found using edge detection command like
Roberts, Sobel, Canny etc. At the same
time, execution time for all the filters is
also calculated so that shortest time taken
edge command or filter command can be
known. The output of each filter and edge
commands are displayed so that the user
can also choose their own choice of edge
command depending upon execution time
or clear output.
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Subhashini A.
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