2 Videos with one spatial dimension 3 2.1 Problem setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Temporal bandwidth analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Sampling implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Experiments within our model assumptions . . . . . . . . . . . ...

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[This corrects the article on p. 556 in vol. 8, PMID: 28446917.].

This paper proposes perceptual compressive sensing. The network is composed of a fully convolutional measurement and reconstruction network. For the following contributions, the proposed framework is a breakthrough work. Firstly, the fully-convolutional network measures the full image which preserves structure information of the image and removes the block effect. Secondly, with the employment ...

A method for online tensor dictionary learning is proposed. With the assumption of separable dictionaries, tensor contraction is used to diminish a N -way model ofO (

Compressive Sensing for 3D Data Processing Tasks: Applications, Models and Algorithms