  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), mutual information 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.
