Color image completion using tensor truncated nuclear norm with l0 total variation

Universitatea din Craiova. Analele. Seria: Matematica, Informatica

article

Karima EL Qate¹  Souad Mohaoui¹  Abdelilah Hakim¹  Said Raghay¹ 

[1] Cadi Ayyad University

关键词: Missing values;    Tensor recovery;    Truncated nuclear norm;    l0 totalvariation;    color images;   

DOI  :  10.52846/ami.v49i2.1532

学科分类：社会科学、人文和艺术（综合）

来源: Universitatea din Craiova / University of Craiova

【 摘 要 】

In recent years, the problem of incomplete data has been behind the appearance of several completion methods and algorithms. The truncated nuclear norm has been known as a powerful low-rank approach both for the matrix and the tensor cases. However, the low-rank approaches are unable to characterize some additional information exhibited in data such as the smoothness or feature-preserving properties. In this work, a tensor completion model based on the convex truncated nuclear norm and the nonconvex-sparse total variation is introduced. Notably, we develop an alternating minimization algorithm that combines the accelerating proximal gradient for the convex step and a projection operator for the nonconvex step to solve the optimization problem. Experiments and comparative results show that our algorithm has a significant impact on the completion process.

【 授权许可】

Unknown