论文标题
图像完成的贝叶斯低级张量环模型
Bayesian Low Rank Tensor Ring Model for Image Completion
论文作者
论文摘要
低等级张量环模型对于图像完成功能强大，可恢复数据采集和转换中缺少的条目。最近提出的张量环（TR）的完成算法通常通过将最小二乘法与预定义的等级交替解决低级优化问题，当未知等级设置得太大并且只有几个测量值时，这很容易导致过度拟合。在本文中，我们通过自动学习数据的低等级结构，为图像完成提供了贝叶斯低级张量环模型。为低级张量环分解开发了乘法相互作用模型，在该模型中，通过假设其条目遵守学生-T分布，核心因素被实施稀疏。与大多数现有方法相比，提出的一种方法是没有参数调整的，并且可以通过贝叶斯推断获得TR等级。数值实验，包括综合数据，具有不同大小的颜色图像以及Yaleface Dataset B相对于一个姿势，表明所提出的方法的表现优于最先进的姿势，尤其是在恢复精度方面。
Low rank tensor ring model is powerful for image completion which recovers missing entries in data acquisition and transformation. The recently proposed tensor ring (TR) based completion algorithms generally solve the low rank optimization problem by alternating least squares method with predefined ranks, which may easily lead to overfitting when the unknown ranks are set too large and only a few measurements are available. In this paper, we present a Bayesian low rank tensor ring model for image completion by automatically learning the low rank structure of data. A multiplicative interaction model is developed for the low-rank tensor ring decomposition, where core factors are enforced to be sparse by assuming their entries obey Student-T distribution. Compared with most of the existing methods, the proposed one is free of parameter-tuning, and the TR ranks can be obtained by Bayesian inference. Numerical Experiments, including synthetic data, color images with different sizes and YaleFace dataset B with respect to one pose, show that the proposed approach outperforms state-of-the-art ones, especially in terms of recovery accuracy.
