摘要
偏振度和偏振角等偏振信息能够反映目标的偏振特性,根据偏振态的差异可以辨别目标,因此偏振信息在水下探测与识别等领域有重要应用。传统的水下偏振成像方法能够对水下图像进行散射抑制并复原光强图像,但这些方法忽略了对偏振信息的复原,造成偏振信息的丢失。因此,提出基于通道注意力机制的水下图像偏振信息复原神经网络,以拓展水下偏振成像的功能,将偏振度和偏振角信息进行复原。同时设计了基于偏振参量的内容与风格损失函数,进一步提升偏振复原效果。实验结果表明,该网络可有效地抑制水下图像的散射光,同时能较好地恢复线性偏振度和偏振角信息。所提方法对浑浊水下偏振成像技术的进一步应用具有重要意义。
Objective Underwater imaging is an important method for exploring oceans,lakes,and other underwater environments,and it is of significance for many fields such as coastal defense,ocean exploration,underwater rescue,and aquaculture.However,there are many suspended particles in the actual water environment,which will scatter and absorb the signal light of the target.Therefore,images obtained by underwater imaging often feature image quality degradation,such as serious contrast reduction and serious detail loss.Due to the differences in polarization characteristics between the signal light and the backscattered light,polarization imaging technology is introduced to underwater imaging and polarization information is employed to suppress scattered light and enhance signal light,which can make up for the shortcomings of detection effects restricted by the environment.Although existing polarization-based descattering methods for underwater imaging can enhance image contrast and improve image quality,these methods only focus on intensity information and ignore polarization information restoration,resulting in a loss of polarization information.In fact,the degree of linear polarization(DoLP)and polarization angle(AoP)among other polarization information can reflect the polarization characteristics of the target.Meanwhile,they are adopted to distinguish targets based on different polarization states and have important applications in underwater detection and recognition.Therefore,we propose a neural network based on the channel attention mechanism to extend the function of underwater polarization imaging by restoring polarization information.Methods The proposed method mainly utilizes a convolutional neural network to restore the polarization information.The network is mainly composed of three parts,including shallow feature extraction module(SFE),a series of residual dense modules(RDBs),and channel attention-based global feature fusion module(CAGFF).Specifically,SFE employs U-Net and two convolutional layers as feature ex
作者
刘贺东
韩宜霖
李校博
程振洲
刘铁根
翟京生
胡浩丰
Liu Hedong;Han Yilin;Li Xiaobo;Cheng Zhenzhou;Liu Tiegen;Zhai Jingsheng;HuHaofeng(School of Precision Instrument and Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China;School of Marine Science and Technology,Tianjin University,Tianjin 300072,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2024年第12期53-62,共10页
Acta Optica Sinica
关键词
偏振成像
水下成像
偏振信息复原
深度学习
注意力机制
polarimetric imaging
underwater imaging
polarization information restoration
deep learning
attention mechanism
