The goal of infrared and visible image fusion in the night scene is to generate a fused image containing salient targets and rich textural details. However, the existing image fusion methods fail to take the unevenness of nighttime luminance into account. To address the above issue, an infrared and visible image fusion method for highlighting salient targets in the night scene is proposed. First of all, a global attention module is designed, which rescales the weights of different channels after capturing global contextual information. Second, the loss function is divided into the foreground loss and the background loss, forcing the fused image to retain rich texture details while highlighting the salient targets. Finally, a luminance estimation function is introduced to obtain the trade-off control parameters of the foreground loss function based on the nighttime luminance. It can effectively highlight salient targets by retaining the foreground information from the source images. Compared with other advanced methods, the experimental results adequately demonstrate the excellent fusion performance and generalization of the proposed method.
Keywords: deep learning; highlighting salient targets; image fusion; infrared images.