Fast infrared and visible image fusion with structural decomposition

Abstract

Infrared and visible image fusion aims to generate a composite image with salient thermal targets and texture information from infrared image and visible image. Existing advanced methods tend to consume high computational cost for a high-quality fusion result. In this paper, a simple yet effective method is proposed based on structural patch decomposition. A modified gamma function is proposed to weight the mean intensity component to keep the thermal target. An enhanced power function is used to weight the mean-removed component to preserve the texture information. Different from general patch level fusion implemented via a sliding window, we convert the explicit structural patch decomposition and fusion into an image level mean filtering via a detailed analysis on input images. By this means, the computational cost of the proposed method can be largely reduced, which is independent of the patch size. Furthermore, we analyze the relationship of our method with classic filtering based image decomposition methods. Finally, a multi-scale implementation of the proposed method is developed to avoid the evident halo and spatial inconsistency artifacts. Extensive experimental results on the public dataset demonstrate that the proposed method can obtain more texture information and outperform the state-of-the-art fusion method qualitatively and quantitatively. The code can be downloaded from: https://github.com/xiaohuiben/MSID-KBS.