Instructor : Dr. Powsiri Klinkhachorn Image Fusion A Fuzzy Logic Approach By Ajit K. Pandey Cpe 521 Instructor : Dr. Powsiri Klinkhachorn

Introduction to Image fusion Image Fusion deals with combining different sources of information. The information are signals delivered by different sensors and images from various modalities. weighted average, neural networks, sub-band filtering, and rules based knowledge. Fuzzy Logic and graph Pyramid methods.

Motivations Need for clear image in various fields to base any decision. Great applications in Medical field to diagnose patients. Various decisions in battle fields are being taken after analysis of various images produced by image fusion .

Evolution Of Image fusion Simple image fusion attempts Pyramid-decomposition-based image fusion Wavelet-transform-based image fusion Pixel Based Image Fusion

Application: Fuzzy Logic Fuzzy approaches are used where there is uncertainty and no mathematical relations are easily available. It improves the reliability by taking care of the redundant information. It improves the capability as it keeps complementary information.

Fuzzy System Main motivation– to reduce the number of rules required The fuzzy System used is Sugeno. This method is also called TSK. Main motivation– to reduce the number of rules required by the Mamdani model For complex and high-dimensional problems Develop a systematic approach to TSK model replaces the fuzzy sets, (then part), of Mamdani rule with function (equation) of the input variables.

Concept Fuzzy approach for pixel level image fusion. This approach forms an alternative to a large number of conventional approaches, which are based on a host of empirical relations. Empirical approaches are time consuming and result in a low correlation.

Algorithm for Pixel level Image fusion Read first image in variable M1 and find its size (rows: z1, columns: S1). Read second image in variable M2 and find its size (rows: z2, columns: s2). Variables MI and M2 are images in matrix form where each pixel value is in the range from 0-255. Here we are using Gray Color map. Compare rows and columns of both input images. If the two images are not of the same size, select the portion which are of same size.

Algorithm contd… Convert the images in column form which has C=z1*s1 entries. Make a fis (Fuzzy) file, which has two input images. Decide number and type of membership functions. Input images in antecedent are resolved to a degree of membership ranging 0 to 255. Make rules for input images, which resolve the two antecedents to a single number from 0 to 255.

Contd… For num=1 to C in steps of one, apply fuzzification This gives a fuzzy set represented by a membership function and results in output image in column format. Convert the column form to matrix form and display the Fused Image.

Image fusion examples Figure shows the two images of the same object from to different sources. remoteA and remoteB are taken as the two input images to be fused. Each image is stored as a matrix that contains their pixel values in the range of 0 to 255.

Output Image Finally we get the fused image from the Fuzzy based Image fusion system as shown

Example 2 Multi-focus image fusion Digital camera application Concealed weapon detection Battle field monitoring

Outcome Fuzzy algorithms have been implemented to fuse a number of images. The two images has been fused and a clear fused image has been obtained containing all the quality features of both the images. The fusions have been implemented for medical images and remote sensing images.

Future Suggestions Fuzzy logic based Image fusion can be applied for obtaining clear images in Satellite applications. It can also be applied for more automatic approach in recent and emerging medical fields.

Summary Image fusion deals with integrating data obtained from different sources of information for intelligent systems. The two images of the same object from different sources are fused using fuzzy algorithm. Fusion provides output as a single image from a set of input images. Empirical approaches are time consuming and result in a low correlation. The military applications include automated target recognition, battlefield surveillance, intelligent mobility, etc.

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