南京邮电大学学报（自然科学版）2024，Vol.44Issue(3)：17-25,9.DOI:10.14132/j.cnki.1673-5439.2024.03.003
基于梯度注意力机制与交叉神经网络的红外与可见光图像融合
Infrared and visible image fusion based on an improved gradient attention mechanism and the cross neural network
摘要
Abstract
Since the current infrared and visible image fusion methods based on deep learning are difficult to distinguish important information from irrelevant information,a new infrared and visible image fusion method based on the gradient attention mechanism and the detail preserving cross network(DPCN)is proposed.First,an improved gradient attention mechanism is introduced into the DPCN to guide the network to focus on the texture details of the visible image and the target information of the infrared image as much as possible.The DPCN is used to enhance the information interaction between the infrared image and the visible image.Then,a decoder based on the multi-scale detail preserving module is proposed to reconstruct the merged features.Finally,an adaptive loss function based on an auxiliary discriminator is designed.The experimental results show that the fusion image of the proposed method can retain clearer edge and target information,and is superior to the compared methods in both subjective and objective evaluations.关键词
图像融合/注意力机制/细节保留交叉神经网络/多尺度图像重建Key words
image fusion/attention mechanism/detail preserving cross network/multi-scale image reconstruction分类
信息技术与安全科学引用本文复制引用
孙希霞,邓林威,潘甦..基于梯度注意力机制与交叉神经网络的红外与可见光图像融合[J].南京邮电大学学报（自然科学版）,2024,44(3):17-25,9.基金项目
国家自然科学基金(62071244,62172235)资助项目 （62071244,62172235）
