LVIF-Net: Learning synchronous visible and infrared image fusion and enhancement under low-light conditions

Abstract

In recent years, the fusion of infrared and visible images has gained significant prominence in the effort to seamlessly integrate salient objects and intricate textures within the fused imagery. While numerous fusion techniques have demonstrated commendable performance, the image quality is notably compromised in low-light environments. This predicament can be attributed to insufficient feature extraction, the inherent low-illumination characteristics of the source images, and the suppression of dark backgrounds. In this study, we introduce a novel approach for simultaneous image fusion and enhancement, aiming to achieve robust fusion performance even in low-light conditions. Our proposed method involves the development of an image fusion network, known as LVIF-Net, which is designed to facilitate multi-layer feature interactions for the enhancement of complementary low-light visible and infrared feature extraction. To solve the problem of low illumination in fused images, we further introduce an Image Decomposition Network (IDN) to guide the LVIF-Net to correct the illumination component of the fused image to obtain enhanced fused images. In addition, for coping with influence of dark background, we design a local region content loss to enhance infrared information in different intensity regions between source images. Extensive experiments on the low-light images on LLVIP and MFNet datasets have shown that the proposed method achieves significant advantages under low-light conditions.