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Quality Assessment of Pixel-Level ImageFusion Using Fuzzy Logic

Author(s): Srinivasa Rao Dammavalam | Seetha Maddala | Krishna Prasad MHM

Journal: International Journal on Soft Computing
ISSN 2229-7103

Volume: 3;
Issue: 1;
Start page: 11;
Date: 2012;
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Keywords: image fusion | wavelet transform | genetic algorithm | fuzzy logic | image quality index | mutual information measure | fusion factor | fusion symmetry | fusion index | entropy.

Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutualinformation measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported methods, wavelet transform based image fusion and weighted average discrete wavelet transform based image fusion using genetic algorithm.

Tango Jona
Tangokurs Rapperswil-Jona

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