NEURAL NETWORKS,CELLULAR NEURAL NETWORKS AND ADAPTIVE FUZZY FILTERS

8/7/2019 NEURAL NETWORKS,CELLULAR NEURAL NETWORKS AND ADAPTIVE FUZZY FILTERS

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By:Devaraja K N Shivanand G Sondakar


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y INTRODUCTIONy CLASSI CAL SPATI AL FILTERSy NEURAL NETWORKSy CELLULAR NEURAL NETWORKSy ADAPTIV E FUZZY FILTERSy RESULTSy CONCLUSI ONy REFERENCES


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y Present day applications require various kinds of images as a sources of information for interpretation and analysis.

y I mages can be contaminated with different types of noise, for differentreasons.

y Whenever an image is converted from one form to another such asdigitizing, scanning, transmitting, storing etc. some form of degradation occur at the output.

y The techniques that we use to improve the visual appearance of imageare

Classical spatial filtersNeural networksCellular neural networksAdaptive fuzzy filters


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y Spatial domain methods are procedures that operatedirectly on contaminated pixels.

y Principle approach to defining a neighborhood about(x, y) is use a square or rectangular sub image areacentered at (x, y).

y The most common neighborhood operation is tomultiply each of the pixels in the neighborhood by aweight and add them together.

y Spatial filters are classified in toLinear filtersNon-linear filters


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y Neural network is a group of elementary process withstrong interconnection.

y Based on learning algorithm of error back propagationa self organizing a three layered feed forward NN isemployed for image enhancement.


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y

Each neuron in a layer is connected to thecorresponding neuron in the previous layer and itsneighbors.


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yy The CNN can be defined as anThe CNN can be defined as an M x N M x N type array of identicaltype array of identicalcells arranged in a rectangular grid. Each cell is locally cells arranged in a rectangular grid. Each cell is locally connected to its 8 nearest surrounding neighbors.connected to its 8 nearest surrounding neighbors.


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y Fuzzy methods may use membership functions, whichcan be defined using two following forms:

The only one membership function may be definedusing the intensity information of total pixels in imageand then apply it for filtering of the all image Pixels.

One adaptive membership function may be defined foreach pixel in the image using the region of supportSm,n;k and weights ar e a ssign ed ba sed on it.


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y Two important features are

The filter estimates a fuzzy derivative in order to beless sensitive to local variations due to image structuressuch as edges.The membership functions are adapted accordingly tothe noise level to perform fuzzy smoothing.


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y OR I GINAL IM AGE y NO ISY IM AGE


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Noisy image

Filtered image of lowpass spatial filters

Filtered image of medianfilters


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Filtered image using

CNN

Noisy image


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NEURAL NETWORKS,CELLULAR NEURAL NETWORKS AND ADAPTIVE FUZZY FILTERS

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