Zhan Weida, Wang Jiale, Jiang Yichun, Chen Yu, Zheng Tingyuan, Hong Yang
National Demonstration Center for Experimental Electrical, School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Entropy (Basel). 2022 Nov 30;24(12):1759. doi: 10.3390/e24121759.
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.
夜间场景中红外与可见光图像融合的目标是生成一幅包含显著目标和丰富纹理细节的融合图像。然而，现有的图像融合方法没有考虑夜间亮度的不均匀性。为了解决上述问题，提出了一种用于突出夜间场景中显著目标的红外与可见光图像融合方法。首先，设计了一个全局注意力模块，该模块在捕获全局上下文信息后重新调整不同通道的权重。其次，将损失函数分为前景损失和背景损失，迫使融合图像在突出显著目标的同时保留丰富的纹理细节。最后，引入一个亮度估计函数，基于夜间亮度获得前景损失函数的权衡控制参数。通过保留源图像中的前景信息，它可以有效地突出显著目标。与其他先进方法相比，实验结果充分证明了所提方法具有优异的融合性能和通用性。
