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
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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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In accession to the binary images, the proposed method may be tested on discrete color images also. These type of
}\}$ be the corresponding Fuzzy sets defined by the membership function $ \{\mu _{A}^{1},\mu _{A}^{2},\mu _{A}^{3}, . . .\mu _{A}^{m}\}$. The implication of the form $\left ( A,{T_{i}^{A}} \right )\rightarrow \left ( B,{T_{j}^{B}} \right )$ or $A\epsilon {F_{i}^{A}},B\epsilon {F_{j}^{B}}$ is a Fuzzy Association Rule.\\
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Step 3: The basic principles in Step 2 were then extended to calculate the degree of possibility of, Ŝi, of one criterion, being greater than all the other (n- 1) convex fuzzy numbers, Ŝj, of other criteria. This can be defined as follows,
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In this chapter, a brief description about medical image fusion with its important terms is provided. This chapter analysis image fusion three algorithms performance through medical application. Analysis of performance of the algorithms is compared in three modes of fusion rules for better medical image fusion. The fusion performance metrics used to utilize the objective of getting better quality medical fused image and detection of better algorithm are PSNR and MSE. The simulation work has been done using MATLB R2011a. [30]
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This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the proposed image fusion technique is divided in the following major phases: preprocesing phase Feature extraction based on the principal component analysis The image fusion based on fuzzy set Reconstruction final image The figure (1) shows the framework of the proposed image fusion and its phases. Fig.