Adaptive Image Filter

8/14/2019 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

X

2

-

-

-

-

X

9


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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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Adaptive Image Filter