Enhancement of image fusion methods

Image fusion methods that can effectively combine information from different imaging sources have become more common due to the growing types and number of imaging sensors, and because it can be difficult for humans to effectively integrate visual information by viewing multiple displays separately. In this paper, we show a novel techniques that runs at the first stage of the fusion process. The algorithm determines the polarity of the images (i.e., assigning brighter or darker gray-level values to larger image intensities), under the assumption that fusion produces better results when the images are more similar. The proposed technique was implemented to various fusion methods, including arithmetic fusion, Lapalce pyramids, Ratio of Low Pass pyramids and Morphological pyramids. Pairs of thermal and visual images were employed in the fusion procedure. The results indicate that the proposed enhancement leads to a better image fusion quality, with low computational cost, according to both objective and subjective fusion evaluations.