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An Improved Image Fusion Technique Based On Texture Feature Optimization Using Wavelet Transform and Teacher Learning Optimsation Technique (TLBO)

Publication Date : 10/07/2015

Author(s) :

Mr. Vipin Wani , Prof. Pankaj Kawadkar.

Conference Name :
4th International Conference on Recent Trends in Engineering & Technology(ICRTET-2015) July 2-4,2015 Organized by SNJB's KBJ College of Engineering,Chandwad,Nashik,Maharashtra,India

Abstract :

Feature based image fusion is most popular area of research in the field of image fusion. The image fusion used lower content of image feature. The lower content of image feature such as color texture and dimension. The very important component of image are texture feature. the extraction and processing of texture feature used various transform function such as wavelet transform function Gabor transform function and many more signal based transform function. In the process of image fusion process requires two and more image for the process of fusion. This dissertation proposed a feature based image fusion technique. The feature based optimization technique also used feature optimization and feature selection process. The feature selection and feature optimization used Teacher learning based optimization technique. The Teacher learning based optimization technique select the optimal texture feature of both image original image and reference image. The reference and original image find the optimal feature sub set for the estimation of feature correlation. Our experimental result shows performance Better instead of WT and HBT transform method of image fusion. Our proposed method increased the value of PSNR and IQI. The improved value of IQI shows that the proposed method is very efficient and better for the process of image fusion. The proposed scheme used for the purpose of wall crack detection.

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