Similarity-based multimodality image fusion with shiftable complex directional pyramid
Pattern Recognition Letters
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In the last few years image fusion has gained considerable attention, where it can provide remarkable outputs for many image applications (\emph{i.e.}, detection of hidden objects). Images with different specifications (resolution, spectral, and spatial) can be fused to produce an output image that combines the best features of all input images. The quality of the output image varies based on the application. In this paper, a new region-based image fusion technique using the Contourlet Transform (CT) is proposed. The presented fusion approach combines the visual information from a visual colored image, and some information about the hidden objects from an IR image. The fused output image is better for human and machine interpretation, where it preserves the original chromaticity of the visual input image. The input images are segmented into small regions more suitable for the proposed algorithm. The segmentation process is performed in the frequency domain. The presented region-based fusion approach is more robust than the traditional pixel-based techniques, where it reduces: the blurring effects, sensitivity to the misregistration problem, and noise effects in the input images. Experimental results demonstrate the capability of the presented fusion technique in detecting hidden weapons and objects. Moreover, the algorithm preserves very high percentage of the input image's spectral components.