Image decomposition is a crucial subject in the field of image processing. It can extract salient features from the source image. We propose a new image decomposition method based on convolutional neural network. This method can be applied to many image processing tasks. In this paper, we apply the image decomposition network to the image fusion task. We input infrared image and visible light image and decompose them into three high-frequency feature images and a low-frequency feature image respectively. The two sets of feature images are fused using a specific fusion strategy to obtain fusion feature images. Finally, the feature images are reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, this method has achieved better performance in both subjective and objective evaluation.

翻译：图像分解是图像处理领域的一个关键主题。 它可以从源图像中提取显著特征 。 我们提议了一种基于卷发神经网络的新图像分解方法 。 这种方法可以应用于许多图像处理任务 。 在本文中, 我们将图像分解网络应用于图像聚合任务 。 我们输入红外图像和可见光图像, 并将其分别分解成三种高频特征图像和低频特征图像 。 两组特征图像使用特定的聚合策略结合, 以获取聚合特征图像 。 最后, 重塑了特征图像, 以获得集成图像 。 与最先进的集成方法相比, 这种方法在主观和客观评估中都取得了更好的效果 。