摘要
针对红外图像和可见光图像配准过程中模态和尺度差异影响下特征点难配准的问题,提出了一种基于模态转换结合鲁棒特征的红外图像和可见光图像配准算法。首先,利用生成对抗网络从可见光图像中生成相应的伪红外图像;其次,通过加速鲁棒特征(SURF)算法提取红外图像的特征点位置信息结合改进的鲁棒特征描述子(PIIFD)实现特征描述;然后,基于Hilbert空间重构的核方法,建立了单高斯鲁棒点匹配模型,在存在异常值的情况下估计映射;最后,采用加权最小二乘法估计变换类型实现图像配准。实验结果表明,与其他算法相比,所提算法可提高红外图像和可见光图像尺度差异大情况下的配准精度,有效配准率达到96%且鲁棒性强。
Aiming at the problem of difficult registration of feature points under the influence of modal and scale differences in the process of infrared image and visible image registration,an infrared image and visible image registration algorithm based on modal transformation and robust features is proposed.First,the generation adversarial network is used to generate the corresponding pseudo infrared image from the visible image;second,the position information of feature points in infrared image is extracted by accelerated robust feature(SURF)algorithm,and the feature description is realized by improved robust feature descriptor(PIIFD);then,based on the kernel method of Hilbert space reconstruction,a single Gaussian robust point matching model is established to estimate the mapping in the presence of outliers;finally,the weighted least square method is used to estimate the transformation type to realize image registration.The experimental results show that compared with other algorithms,the proposed algorithm can improve the registration accuracy in the case of large scale difference between infrared image and visible image,the effective registration rate is 96%and has strong robustness.
作者
杨冰超
王鹏
李晓艳
李亮亮
曹小芳
Yang Bingchao;Wang Peng;Li Xiaoyan;Li Liangliang;Cao Xiaofang(School of Ordnance Science and Technology,Xi’an Technological University,Xi’an,Shaanxi 710021,China;Electronic Information Engineering,Xi’an Technological University,Xi’an,Shaanxi 710021,China;School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou,Gansu 730070,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第4期172-181,共10页
Laser & Optoelectronics Progress
基金
国家自然基金重点项目(62031021,61671362)
陕西省科技厅重点研发计划(2019GY-022)
西安市科技计划项目(2020KJRC0037)
西安市未央区科技计划项目(201923)
西安工业大学校长基金面上培育项目(XGPY200217)。
关键词
图像处理
图像配准
鲁棒点匹配
特征提取
生成式对抗网络
image processing
image registration
robust point matching
feature extraction
generative adversarial network
