| Abstract:
Tensor (matrix) completions have wide applications in fields such as
computer vision and image processing. To achieve completion, most existing
methods are based on singular value decomposition and nuclear norm
minimization of real tensors. However, these tensor completion methods
cannot simultaneously maintain the color channel correlation and evolution
robustness of color video frames, and require high computational costs to
process high-dimensional data. Therefore, they have some limitations in
model generalization ability and computational efficiency. In this talk,
through the definition of QR decomposition and the new quaternion tensor
norm, a new quaternion tensor (matrix) completion method is explored, which
can well balance the model generalization ability and efficiency, and the
performance of this completion is significantly improved. Numerical
experiments on color images and videos prove the effectiveness of our
proposed method.
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