RAN: Infrared and Visible Image Fusion Network Based on Residual Attention Decomposition

Abstract

Infrared image and visible image fusion (IVIF) is a research direction that is currently attracting much attention in the field of image processing. The main goal is to obtain a fused image by reasonably fusing infrared images and visible images, while retaining the advantageous features of each source image. The research in this field aims to improve image quality, enhance target recognition ability, and broaden the application areas of image processing. To advance research in this area, we propose a breakthrough image fusion method based on the Residual Attention Network (RAN). By applying this innovative network to the task of image fusion, the mechanism of the residual attention network can better capture critical background and detail information in the images, significantly improving the quality and effectiveness of image fusion. Experimental results on public domain datasets show that our method performs excellently on multiple key metrics. For example, compared to existing methods, our method improves the standard deviation (SD) by 35.26%, spatial frequency (SF) by 109.85%, average gradient (AG) by 96.93%, and structural similarity (SSIM) by 23.47%. These significant improvements validate the superiority of our proposed residual attention network in the task of image fusion and open up new possibilities for enhancing the performance and adaptability of fusion networks.