An improved fusion algorithm for infrared and visible images based on multi-scale transform

• Morphology-hat transform is used on source images to enhance images contrast.
• Contourlet transform is used to decompose the original image into multi-scale and multi-direction images.
• Different fusion strategies are used on the low-frequency images and high-frequency images to obtain better fused effect.
• Three experiments are performed to compare the proposed method with other current methods.
• Subjective and objective evaluations are given.

In this paper, an improved fusion algorithm for infrared and visible images based on multi-scale transform is proposed. First of all, Morphology-Hat transform is used for an infrared image and a visible image separately. Then two images were decomposed into high-frequency and low-frequency images by contourlet transform (CT). The fusion strategy of high-frequency images is based on mean gradient and the fusion strategy of low-frequency images is based on Principal Component Analysis (PCA). Finally, the final fused image is obtained by using the inverse contourlet transform (ICT). The experiments and results demonstrate that the proposed method can significantly improve image fusion performance, accomplish notable target information and high contrast and preserve rich details information at the same time.