EV-Fusion: A Novel Infrared and Low-Light Color Visible Image Fusion Network Integrating Unsupervised Visible Image Enhancement

Abstract

Infrared and visible image fusion can effectively integrate the advantages of two source images, preserving significant target information and rich texture details. However, most existing fusion methods are only designed for well-illuminated scenes and tend to lose details when encountering low-light scenes because of the poor brightness of visible images. Some methods incorporate a light adjustment module, but they typically focus only on enhancing intensity information and neglect the enhancement of color feature, resulting in unsatisfactory visual effects in the fused images. To address this issue, this paper proposes a novel method called EVfusion, which explores the potential color and detail features in visible images and improve the visual perception of fused images. Specifically, an unsupervised image enhancement module is designed that effectively restores texture, structure and color information in visible images by several non-reference loss functions. Then, an intensity image fusion module is devised to integrate the enhanced visible image and the infrared image. Moreover, to improve the infrared salient object feature in the fused images, we propose an infrared bilateral-guided salience map embedding into the fusion loss functions. Extensive experiments demonstrate that our method outperforms state-of-the-art infrared visible image fusion methods.