Fusion of images from different imaging modalities, obtained by conventional fusion methods, may cause artifacts, including destructive superposition and brightness irregularities, in certain cases. This paper proposes two methods for improving image multimodal fusion quality. Based on the finding that a better fusion can be achieved when the images have a more positive correlation, the first method is a decision algorithm that runs at the preprocessing fusion stage and determines whether a complementary gray level of one of the input images should be used instead of the original one. The second method is suitable for multiresolution fusion, and it suggests choosing only one image from the lowest-frequency sub-bands in the pyramids, instead of combining values from both sub-bands. Experimental results indicate that the proposed fusion enhancement can reduce fusion artifacts. Quantitative fusion quality measures that support this conclusion are shown.
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.
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