红外技术2024，Vol.46Issue(3)：288-294,7.
基于双支路拮抗网络的偏振方向图像融合方法
Fusion Method for Polarization Direction Image Based on Double-branch Antagonism Network
摘要
Abstract
To improve the quality of the fused image,the study presents a double-branch antagonism network(DANet)for the polarization direction images.The network includes three main modules:feature extraction,fusion,and transformation.First,the feature extraction module incorporates low and high-frequency branches,and the polarization direction images of 0°,45°,90°,and 135° are concatenated and imported to the low-frequency branch to extract energy features.Two sets of polarization antagonism images(0°,90°,45°,and 135°)are subtracted and entered into the high-frequency branch to extract detailed features and energy.Detailed features are fused to feature maps.Finally,the feature maps were transformed into fused images.Experiment results show that the fusion images obtained by DANet make obvious progress in visual effects and evaluation metrics,compared with the composite intensity image I,polarization antagonistic image Sd,Sdd,Sh,and Sv,the average gradient,information entropy,spatial frequency,and mean gray value of the image are increased by at least 22.16%,9.23%,23.44%and 38.71%,respectively.关键词
图像融合/深度学习/偏振图像Key words
image fusion/deep learning/polarization image分类
计算机与自动化引用本文复制引用
凤瑞,袁宏武,周玉叶,王峰..基于双支路拮抗网络的偏振方向图像融合方法[J].红外技术,2024,46(3):288-294,7.基金项目
国家自然科学基金资助项目(61906118) （61906118）
安徽省自然科学基金资助项目(2108085MF230) （2108085MF230）
偏振光成像技术安徽省重点实验室开放基金(KFJJ-2020-2) （KFJJ-2020-2）
