基于非局部张量火车分解的彩色图像修补  

Nonlocal Similarity Based Tensor Train Factorization for Color Image Completion

作　　者：贾慧迪 韩志[1,2] 陈希爱 唐延东 JIA Huidi;HAN Zhi;CHEN Xiai;TANG Yandong(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110016;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,沈阳110016 [2]中国科学院机器人与智能制造创新研究院,沈阳110016 [3]中国科学院大学,北京100049

出　　处：《模式识别与人工智能》2019年第10期955-963,共9页Pattern Recognition and Artificial Intelligence

摘　　要：数据在采集和转换的过程中通常存在部分数据丢失的问题,丢失数据的补全直接影响后续的识别、跟踪等高层任务的结果.自然图像中经常存在许多具有重复特性的相似结构,利用该类冗余信息,文中提出基于非局部张量火车分解的张量补全方法.利用图像的非局部相似性,挖掘其中蕴含的低秩特性,并通过张量火车分解模型进行建模及升阶,将低阶张量转化为高阶以进行低秩信息的进一步挖掘利用,从而进行图像中缺失数据的修补.实验验证文中方法在图像修补上的有效性.In data acquisition and transformation,the data are more or less lost.Therefore,the results of computer vision tasks such as object recognition and tracking are affected.In a natural image,there are many similar structures and patterns with repeated features.With these similar structures and patterns,a method of nonlocal similarity based tensor train factorization for color image completion is proposed.Nonlocal similarity of images are employed to exploit the low rank feature,and modeling is conducted by tensor train factorization to further mine low rank information through transforming a low-order tensor to higher-order one.Experimental results validate the proposed method in image completion.

关 键 词：张量火车分解 非局部相似性 低秩性 图像修补 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]