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Noise ReductionandImage Sharpening Using Linear Spatial Filteringin Plant
Leaves Disease Detection
Dr.K.Thangadurai,Asst. Prof.,
PG & Research Dept. Of Comp. Science
Government Arts College (Autonomous)Karur
- 639 005, TamilNadu
K.Padmavathi
Ph.DResearch Scholar
Research &Development centre
Bharathiar University, CoimbatoreTamilNadu
Abstract
Filtering is the process of noise reduction or the
sharpening of which is used to enhance the quality of
the images. The identification and detection of plant
leaves detection depends on the leaves image
quality.Noises in the leaves images give greater
difficulty for detecting the diseases. The captured
images are used for identifying and detecting the
diseases. These types of images are not perfect quality
and may be degraded and corrupted due to variations of the environment. So, filtering process is used to get a
reliable image in plant leaves diseases detection.
Filtering is used for noise elimination, noise
smoothening, lines and shading removal that can
interfere with the recognition process. The filtering
processis based on purpose of the process, the type of
noise and on the amount or intensity of noise contained
in an image. Here, the gray scale images are used for
plant leaves detection and laplacian spatial filtering is
used to improve the quality of the plant leaves images.
Therefore, this paper describes the uses of the laplacian
filter in plant leaves detection.
Keywords: Spatial domain, Filtering, Laplacian filter,
noise, Sharpening, Plant leaves, filtering process.
1. Introduction The captured images are used in detection of plant
leaves diseases. The captured images have unwanted
noise or interference. Noisy images are not suited for
plant leaves diseases detection and analysis and this
creates problem for interpretation of images. Hence
noise should be removed from the images. There are
different types of noises are appeared. They are grouped
into:additive noise, Gaussian noise, salt and pepper and
Poisson noise etc.Linear spatial filtering can be useful
to recover the problem.
Linear spatial filtering is a pixel by pixel transformation
which depends on the number of surrounding pixels.
Linear spatial filtering is a context dependent operation
that alters the grey level of a pixel
at any location according to its relationship with digital
counts of the pixels.
Especially, linear spatial filter laplacian is very suitable
for reducing noise, sharpening ofgray scale images in
leaves diseases detection.
2.Related Works
Image noise is unwanted information that isoccurred
during the image capture, transmission, processing. In
images, the noise can be modelled as Gaussian,
uniform, Poisson or salt-and-pepper distribution [1].
Sequences of image enhancement techniques are used
to improve the quality of the images and to give a
solution for image processing problems. These
techniques use low illumination and high magnification
where noise problems are associated. For this reason,
noise removal and image sharpening is an important image processing technique [2, 3, 4].
There are various types of noise that occurred and
corrupt the images such as additive noise,Gaussian
noise, salt and pepper and Poisson noise etc.and various
filters are used to these type of noises. They are:
▪ Gaussian filter
▪ Laplacian filter
▪ High pass filter
▪ Low pass filter
Noise can be removed and we can get enhanced high
quality imageby using these types of filters[5].
Filtering is a mathematical process where the intensity
of one pixel value is modified or combined with the
intensity of neighbourhood pixels[6]. A filter also
enhances frequencies to visualize in the frequency
domain which are used to noise removal, image
sharpening and edge enhancement [7]. There is no common theory for image
enhancement.Image enhancement approaches come
under two categories:
▪ Spatial domain
▪ Frequency domain
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Spatial domain filtering methods operate directly on
thepixels of an image and use spatial masks or kernels
for image enhancement [4, 8]. In spatial domain
filtering, the pixel values are manipulated to achieve
expected enhancement[1].
Spatial domain filtering is an important enhancement
technique which is used to filter noises and image
sharpening. If the processes or calculations are
performed on the pixels of the neighbours are called linear spatial filter.These linear spatial filters operate on
small neighbourhood 3 x 3 to 11 x 11[9].
3. SpatialFiltering
A). Process of Filtering
Filtering is the process which is performed by using
convolution windows. These windows are called mask
or kernel. The window is moved over the input image
from pixel to pixel which is performing discrete
mathematical function transforming the original input image digital value to a new value.
Convolution is amathematical process which is one of
the fundamental image processing operations.
Convolution provides a way of multiplexing together
two arrays of numbers which are different sizes, but the
same dimensionality, which are producing a third array
of number of same dimensionality. In image processing,
one of the input array is normally a gray level image
and the second array is much smaller and is known is
the kernel or mask [fig.1].
The convolution process is performed by the kernel
over the image which is starting at the top left corneri.e.
the kernel moves all positions in the input image. Each
kernel position corresponds to a single output pixel and
the output value is calculated by multiplying together
the kernel value and underlying image pixel value for
each cell in the kernel, and then adding all these
numbers together.If the image size has M x N and the kernel size has m x n, then the size of the output image
will have M-m+1 rows and N-n+1 columns.
The convolution process is written as:
m n
O(i,j)=Σ ΣI(i+k-1,j+i-1) x K(k,1)
K=1j=1Here, I runs from 1 to M-m+1 and j runs from 1
to N-n+1. A kernel window may be rectangular (1x3 or 1x5
pixels) size or square (3x3,5x5 or 7x7 pixels) size.Each
pixel of the window is assigned a weight.
B). Spatial Filtering
Spatial filtering moves across pixel by pixel in the input
image and places resulting pixels into the output image.
The steps involved in the spatial filtering are:
1. Select the current pixel and mask
2. Position the mask over the current pixel
3. Perform an operation with the respective
elementsof the neighbourhood
4. Letting the result of that operation
5. Repeating this process for every point
Fig.1. Filtering process
Spatial filtering uses a convolve kernel or mask which
containing an array of convolution coefficient values is
called key elements. The kernel or mask size can be 1 x
1, 3 x 3, 5 x 5, M x N and so on. A larger kernel size
affords a more precise filtering operation by increasing
the number of neighbouring pixels used in the
calculation. However, the kernel cannot be bigger in
any dimension than the image data.
Spatial filtering has two different categories:
1.Linear Spatial Filtering
2.Nonlinear Spatial Filtering In linear spatial filtering, the process consists of moving
the centre of the filter mask from point to point in an
input image. Nonlinear spatial filtering is also based on
neighbourhood operations and the mechanics of
defining m x n neighbourhoods by sliding the centre
point through an image. However, linear spatial filtering
process is based on computing the sum of products i.e.
linear operation and nonlinear spatial filtering process
involves the pixels of a neighbourhood and letting the
response at each centre point be equal to the maximum
pixel value in its a neighbourhood.
The laplacian filter is one of the linear spatial filters
which are used to enhance the quality of the gray scale
images. The laplacian filter subtracts the brightness
values of the four neighbouring pixels from the central
pixel. The result of applying this filter is to reduce the
gray level to zero. This paper describes the uses of laplacian filter in plant leaves diseases detection.
4. Proposed Approach
A). Laplacian Filter A Laplacian filter is a second order derivative non-
directional filter as it enhances the linear features of the
K Padmavathi et al, Int.J.Computer Technology & Applications,Vol 5 (4),1561-1565
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images.This approach uses a discrete formulation of the
secondorder derivative and constructs a filter mask
based on that formulation.Laplacian filtering
emphasizes maximum values within the image by using
a kernel with a central value typically surrounded by
negative weights in the north-south and east-west
directions and zero values at the kernel corners.
The Laplacian filter is an image sharpening filter that
works well for noise-free images. This filter subtracts the brightness values of the four neighboring pixels
from the central pixel and the result of applying this
filter is to reduce the gray level to zero.
The Laplacian of any function fis given by :
----- (1)
Here, and are the
second order derivatives of fin x and y direction
respectively.We consider the equation for thepartial
secondorder derivative in the x-direction is:
and in the y-direction is:
The implementation of the two-dimensional Laplacian
in Eq. (1) isobtained by summing of x and y
components:
∇2f = f (x +1, y) + f (x −1, y) + f (x, y +1) +
f(x, y −1) − 4 f (x, y)
This equation is implemented at all points(x,y) in an
image by convolving the image with the following
spatial mask whichgives a result for rotations in
increments of 90°.
This can be represented as:
Fig.2.Representation of Laplacian Filter
The Laplacianfiltercomputes the differences between
digital counts of the central pixel and the average values
of four adjacent pixels in the horizontal and vertical
location. This can be written as:
Y = (X-a4) + (X-a5) + (X-a2) + (X-a7)
Then, the output image is obtained using the sum of the
partial differences in the horizontal and vertical pixels.
We consider an alternate mask which takes the diagonal
elements and this mask gives a result for increments of
45°.
This window takes derivatives in eight orientations i.e.
horizontal, vertical and two diagonal
directions.Laplacian filter enhancement is based on the
equation:
g (x, y) = f (x, y) + c [∇2 f(x, y)]
Where f(x, y) is the input image and g(x, y) is the
enhanced output image and c is the centre co-efficient
of the mask. This second order derivation
isimplemented in the plant leaves diseases detectionwhich is used to get the noise removed,
enhanced andsharpened images.
Fig.3. (a) Original image (b) Laplacian using unit8
(c) Laplacian using double (d) Enhanced image using
formatLaplacian -4 in the center
(c) Enhanced image using Laplacian -8 in the center
K Padmavathi et al, Int.J.Computer Technology & Applications,Vol 5 (4),1561-1565
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ISSN:2229-6093
Fig.4. (a) Original image (b) Laplacian using unit8
(c) Laplacian using double (d) Enhanced image using
formatLaplacian -4 in the center
(e) Enhanced image using
Laplacian -8 in the center
Using the Laplacian filter, the blurred and noised
original diseased plant leaves are enhanced and
sharpened. These types of images are very suitable for
analysis and detection of plant leaves diseases.
The above sample diseased plant leaves enhancement
process shows that the Laplacian filter is used a
negative centrecoefficient i.e. the subtraction operation
and obtained the sharpened enhanced image as a result.
B). MATLAB Implementation
Various diseased plant leaves were taken and Laplacian
filter process was applied and tested using MATLAB
R2010a. First different types diseased plant leaves were
considered and Laplacian filtering functions were
implemented using various masks. We got the noise
removed and sharpened leaves image based on the mask
values and image initial quality.
5. CONCLUSION
The Laplacian is a linear operator which has zero
response to linear ramps but it responds to the shoulders
at the top and bottom of a ramp.From the results, the
Laplacian filter responds to noise and it responds
strongly to corners i.e. corners, lines and isolated points
and it gives enhanced sharpened images as a result
based on the spatial mask and quality of the image.
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