Infrared and visible image fusion via gradientlet filter

In this paper, we propose an image filter based on fuzzy gradient threshold function and global optimization, termed as gradientlet filter, from the perspective of luminance and gradient separation. It can remove small gradient textures and noise while maintaining the overall brightness and edge gradients of an image. Based on gradientlet filter and image saliency, we further put forward a new method for infrared and visible image fusion, which can overcome the challenges of low contrast, edge blurring and noise existing in traditional fused images. First, the gradientlet filter is used to decompose source images into approximate layers and residual layers, where the former reflects the overall brightness of source images without edge blurring and noise, and the latter reflects the small gradient texture and noise of source images. Second, according to the characteristics of the approximate and residual layers, we propose contrast and gradient saliency maps and construct corresponding weight matrices. Finally, the fused image is obtained by fusion and reconstruction based on previously obtained sub-images and weight matrices. Extensive experiments on publicly available databases demonstrate the advantages of our method over state-of-the-art methods in terms of maintaining image contrast, improving target saliency, preventing edge blurring, and reducing noise.