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mage fusion based on fuzzy sets

The fuzzy logic approach is widely used in image process-ing. The fuzzy logic gives decision rules and fusion motivation for image fusion [17]. the two inputs images are converted into membership values based on a set of predefined MFs, where the degree of membership of each input pixel to a fuzzy set is determined. Then, the fusion operators are applied to the fuzzified images. The fusion results are then converted back into pixel values using defuzzification.

1) Fuzzy sets: The fuzzy sets are used to describe the gray levels of the input images. we have two inputs and one output. the two inputs are ; the first input is the Pan image and the second input is the first principal component( PC

1

) of the MS*…show more content…*

The Mamdani fuzzy inference is widely used in applications, because of it has the simple structure of defuzzification method Mamdani type min-imum sum mean of maximum which is used.Defuzzification refers to the way a crisp value is extracted from a fuzzy set as a representative value. The fuzzy rules in the form IF-THEN is used .The If-Then type fuzzy rules converts the fuzzy input to the fuzzy output.

These rules are designed in the form of combination of inputs (Pan and pc

1

) represents as : (z) = max(x;y) =)fL;M!Mg (11) where x and y represenst pixel gray level values of Pan and

PC

1 images respectively.The meaning of equation (11) that the pan gray level is low and the gray level of pc

1

is meduim then the gray level of the fused image is meduim. we have 25 rules to fuse the pan image and PC

1

we summerize as following :

TABLE I

FUZZY RULES OF IMAGE FUSION FUZZY LOGIC

VL L M H VH

L L M H VH

M M M H VH

H H H H VH

VH VH VH VH VH

The algorithm of image fusion by using fuzzy sets is implemented as the following:

Algorithm 2fuzzy logic image fusion algorithm

1: Input: M1 and M2

2: read first image in variable M1 ( Pan image) and calculate its size (rows : m1 and columns: n1)

3: read second image in variable M2 ( PC

1

); and calculate its size (rows : m2 and columns: n2)

4: M1and M2 Variables are images in matrix form where each pixel value is in the range from 0-255.

5: Compare the size of both input images. If the two images are not of the

The fuzzy logic approach is widely used in image process-ing. The fuzzy logic gives decision rules and fusion motivation for image fusion [17]. the two inputs images are converted into membership values based on a set of predefined MFs, where the degree of membership of each input pixel to a fuzzy set is determined. Then, the fusion operators are applied to the fuzzified images. The fusion results are then converted back into pixel values using defuzzification.

1) Fuzzy sets: The fuzzy sets are used to describe the gray levels of the input images. we have two inputs and one output. the two inputs are ; the first input is the Pan image and the second input is the first principal component( PC

1

) of the MS

The Mamdani fuzzy inference is widely used in applications, because of it has the simple structure of defuzzification method Mamdani type min-imum sum mean of maximum which is used.Defuzzification refers to the way a crisp value is extracted from a fuzzy set as a representative value. The fuzzy rules in the form IF-THEN is used .The If-Then type fuzzy rules converts the fuzzy input to the fuzzy output.

These rules are designed in the form of combination of inputs (Pan and pc

1

) represents as : (z) = max(x;y) =)fL;M!Mg (11) where x and y represenst pixel gray level values of Pan and

PC

1 images respectively.The meaning of equation (11) that the pan gray level is low and the gray level of pc

1

is meduim then the gray level of the fused image is meduim. we have 25 rules to fuse the pan image and PC

1

we summerize as following :

TABLE I

FUZZY RULES OF IMAGE FUSION FUZZY LOGIC

VL L M H VH

L L M H VH

M M M H VH

H H H H VH

VH VH VH VH VH

The algorithm of image fusion by using fuzzy sets is implemented as the following:

Algorithm 2fuzzy logic image fusion algorithm

1: Input: M1 and M2

2: read first image in variable M1 ( Pan image) and calculate its size (rows : m1 and columns: n1)

3: read second image in variable M2 ( PC

1

); and calculate its size (rows : m2 and columns: n2)

4: M1and M2 Variables are images in matrix form where each pixel value is in the range from 0-255.

5: Compare the size of both input images. If the two images are not of the

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