The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initiali...

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 (

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

The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the different forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.