IJTC201601002-Adaptive Gaussian Filter Based Image Recovery Using Local Segmentation
Adaptive Image Filter
Transcript of Adaptive Image Filter
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If the brain were so simple that
we could understand it then wed
be so simple that we couldnt
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ADAPTIVE DIGITAL
IMAGE FILTER
DEVELOPMENT OF
FLANN BASED
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1)IMAGE AND IMAGE PROCESSING
2)FILTERS AND TYPE OF FILTERS
3)DIFFERENT TECHNIQUES USEDTO DESIGN A FILTER
4)BEST TECHNIQUE
TOPICS TO BE DISCUSSED
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What is Image?
The term image refer to a twodimensional light intensity function
f(x,y), where x and y denote spatial co-ordinates and the value of f at any point(x,y) is proportional to the brightness (orgray level) of the image at that point.
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What is Image
processingImage processing is any form of
information processing for whichthe input is an image, such asphotographs or frames of video;
the output is not necessarily animage, but can be for instance aset of features of the image.
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Image Processing
Techniques
Image EnhancementImage RestorationImage Compression
Image Segmentation
Image Acquisition
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What is Image
Restoration It is an Image processing technique for
getting an original image from a noisy
image.It is of two 2 Types:
1.Image Denoising
2. Image Deblurring
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Different filter used for
Image Denoising
1.Linear Filters
2.Non-Linear filter
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Drawbacks of linearfilter
Linear filters generally have Linear
characteristics. But on online application the noise
added to the system is adaptive in nature. On that
case the linear filter wont able to denoise.
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Non linear filter
In order to restore the image from
adaptively degraded image, we are
generally using non linear filter.
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Different Algorithms For
Designing Non Linear FiltersNeural Network
Fuzzy logic
Ant colony
Bacteria Technology
Genetic algorithm
But we prefer Neural Network
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It is an interconnected group of artificial neuronthat use a mathematical model or computational
model for information processing based on aconnectionist approach to computation.
Its main aim is to mimic the human ability to adapt to
changing circumstances and current environment.
INTRODUCTION TO
NEURAL NETWORK
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Basic Concepts
Neural Network
Input 0 Input 1 Input n...
Output 0 Output 1 Output m...
A Neural Networkgenerally maps a set ofinputs to a set of outputs
Number of inputs/outputsis variable
The Network itself iscomposed of an arbitrarynumber of nodes with anarbitrary topology
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What can Neural Network do?
Compute a known function
Approximate an unknown function
Pattern Recognition Signal Processing
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NEURAL NETWORKS ARE CATEGORISED
MAINLY INTO THREE PARTS
1)SINGLE LAYER
PERCEPTRON
2)MULTI LAYER
PERCEPTRON
3) FLANN - FUNCTIONAL LINK
ARTIFICIAL NEURAL NETWORK
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Single Layer Perceptron
(9-1 network)
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MULTI LAYER
9-4-1 Neural Network
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FLANN
LMS
y
d
e
W
X
1
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DIFFERENT FUNCTIONAL
ENHANCING PATTERNS ARE :Exponential expansion
Trigonometric Functional expansion
Power series expansion
Chebyshev expansion
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WORKING WITH SINGLE LAYERPERCEPTRON
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WORKING WITH MULTI LAYERPERCEPTRON
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WORKING WITH FLANN
ORIGINAL IMAGEDISTORTED IMAGE
Image corrupted with
Gaussian Noise of mean0 and variance 0.01
RESTORED IMAGE
USING FLANN
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COMPARISION OF MLP &FLANN
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Status of Neural Networks
Most of the reported
applications are still in research
stage
No formal proofs, but they seem
to have useful applications thatwork
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ConclusionIt can concluded that the performance of FLANN
is better than MLP for noise suppression from an
image. The FLANN structure may be used for
online image processing application due to its less
computational requirement and satisfactory
performance.
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References
[5.1] P.J.Antsaklis,"Neural network in control system."IEEE,
Control.Syst.Mag.PP. 3-5. April.1990.
[5.2] S. Haykin. Neural Networks. Ottawa.ON.Canda Maxwell Macmillan.
1994.
[5.3] P.S.Sastry.G . Santharam and K.P. Unnikrishnan." Memoryneural
networks for identification and control of dynamical systems." IEEE
Trans.Neural Networks. vol. 5.pp. 306-319.Mar.1994.[5.4] A.G. Parlos.K.T.Chong and A.F .Atiya."application of recurrent
multilayer perceptron in modeling of complex process dynamics."
IEEE Trans. Neural Networks .vol.5,pp255-266,Mar.1994.
[5.5] R Grino.G.Cembrano and C.Torres."Nonlinear system
identification using additive dynamic neural networks two on line
approaches."IEEE Trans Circuits SystemIvol47 150-165.Feb 2000.
[5.6] T.Poggio and F.Girosi."Networks for approximation and
learning."Proc. IEEE ,vol 78,pp.1481-1497,sep1990.
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TUHIN GHOSE
&KEERTI PRAKASH PARIJA
TRIDENT ACADEMY OF
TECHNOLOGY
BHUBANESWAR
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