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Visible-light image and infrared image fusion technology can be used to obtain an image that has both the detailed texture of the visible-light image and the characteristics of infrared heat radiation. However, most existing fusion algorithms inevitably introduce noise from the source image during fusion. To solve this problem, an image fusion algorithm based on a multiscale transformation framework is proposed in this study. This algorithm first uses multiscale transformation theory to decompose the source images, and then uses a method based on the standard deviation of the local area as the fusion rule of the low-frequency components. In addition, a visual saliency detection method based on guided filtering is used to extract weight mapping of the high-frequency components, which are weighted and fused according to the distribution of the weight mapping. Finally, the components are inversely transformed to obtain a visible light–infrared fusion image. The experiments show that the proposed algorithm can effectively maintain the detailed texture of visible-light images, as well as infrared heat radiation information, and has the advantages of low noise, high contrast, and a better visual effect.
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