Bayesian Low Rank Tensor Ring Model for Image Completion
This addresses the challenge of overfitting in image completion for applications like data acquisition, though it is incremental as it builds on existing tensor ring methods.
The paper tackles the problem of image completion using a low rank tensor ring model, proposing a Bayesian approach that automatically learns the low rank structure without manual parameter tuning, and it outperforms state-of-the-art methods in recovery accuracy on synthetic data, color images, and the YaleFace dataset.
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.