- 期刊
- OpenAccess
Compressive Sensing of Sparse Signals and MR Images
並列摘要
The sparsity of a signal means that it can be represented by a small number of non-zero coefficients in a certain basis. The reconstruction of a sparse signal can be done from sub-Nyquist samples by using nonlinear optimization, which is known as ＂compressive sensing (CS)＂. CS is a very promising technique in wide range of areas e.g.wireless healthcare systems, medical imaging such as MRI since CS enables low-power and cost-effective data processing. Most of these applications have been possible since the real world signals such as sound, image are inherently sparse. In this work, a Graphical User Interface (GUI) is developed in Matlab which can be used to do CS based reconstruction of sparse signals and MR images. This program is devoted to the scientists and researchers who desire to explore the quality of reconstruction from sub-Nyquist data and the effect of the parameters in the algorithm based on CS.