Abstract
Tensor Compressive Sensing (CS) is an emerging approach for higher order data representation, such as medical imaging, video sequences and multi-sensor networks. In this paper, we propose an Adaptive Tensor CS (ATCS) scheme for Three-dimensional (3D) images, especially those which contain noise. First, we find the relationship between reconstruction performance, noise level and sampling rate. Second, we develop the ATCS method by implementing a noise estimation algorithm. Finally, we apply the method in the CS system for efficient representation of 3D video sequences. We also demonstrate experimentally that ATCS outperforms other state of the art algorithms.
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