Infrared-visible image fusion using the undecimated wavelet transform with spectral factorization and target extraction

In this work we propose a fusion framework based on undecimated wavelet transforms with spectral factorization which includes information about the presence of targets within the infrared (IR) image to the fusion process. For this purpose a novel IR segmentation algorithm that extracts targets from low-contrast environments and suppresses the introduction of spurious segmentation results is introduced. Thereby, we ensure that the most relevant information from the IR image is included in the fused image, leading to a more accurate representation of the captured scene. Moreover, we propose the use of a novel, hybrid fusion scheme which combines both pixeland region-level information to guide the fusion process. This turns the fusion process more robust against possible segmentation errors which represents a common source of problems in region-level image fusion. The combination of these techniques leads to a novel fusion framework which is able to improve the fusion results of its pure pixel-level counterpart without target extraction. Additionally, traditional pixel-level fusion approaches, based on state-of-the-art transforms such as the Nonsubsampled Contourlet Transform and the Dual-Tree ComplexWavelet Transform, are significantly outperformed by the use of the proposed set of methods.