基于非局部张量火车分解的彩色图像修补

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.

Nonlocal Similarity Based Tensor Train Factorization for Color Image Completion

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.