On Collaborative Compressive Sensing Systems: The Framework, Design and Algorithm

@article{Zhu2018OnCC,
  title={On Collaborative Compressive Sensing Systems: The Framework, Design and Algorithm},
  author={Zhihui Zhu and Gang Li and Jiajun Ding and Qiuwei Li and Xiongxiong He},
  journal={SIAM J. Imaging Sci.},
  year={2018},
  volume={11},
  pages={1717-1758}
}
We propose a collaborative compressive sensing (CCS) framework consisting of a bank of $K$ compressive sensing (CS) systems that share the same sensing matrix but have different sparsifying dictionaries. This CCS system is guaranteed to yield better performance than each individual CS system in a statistical sense, while with the parallel computing strategy, it requires the same time as that needed for each individual CS system to conduct compression and signal recovery. We then provide an… Expand
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References

SHOWING 1-10 OF 65 REFERENCES
Designing robust sensing matrix for image compression
TLDR
The results show that the sensing matrix obtained using the proposed approach outperforms those existing ones using a fixed dictionary in terms of signal reconstruction accuracy for synthetic data and peak signal-to-noise ratio for real images. Expand
Alternating Optimization of Sensing Matrix and Sparsifying Dictionary for Compressed Sensing
TLDR
The proposed CS system yields in general a much improved performance than those designed using previous methods in terms of peak signal-to-noise ratio for the application to image compression. Expand
Distributed Compressive Sensing
TLDR
A new theory for distributed compressive sensing (DCS) is introduced that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-Signal correlation structures. Expand
On joint optimization of sensing matrix and sparsifying dictionary for robust compressed sensing systems
TLDR
Simulation and experiments show that the PSR scheme and the CS system, obtained using the proposed approaches, outperform the prevailing ones in terms of reducing the effect of sparse representation errors. Expand
Sensing Matrix Optimization for Block-Sparse Decoding
TLDR
This work proposes a framework for sensing matrix design that improves the ability of block-sparse approximation techniques to reconstruct and classify signals by minimizing a weighted sum of the interblock coherence and the subblocks coherence of the equivalent dictionary. Expand
Optimized Projection Matrix for Compressive Sensing
TLDR
A method is proposed to optimize the projection matrix based on equiangular tight frame (ETF) design, which demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit. Expand
A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing
TLDR
An alternating minimization approach for this purpose which is a variant of Grassmannian frame design modified by a gradient-based technique to optimize an initially random measurement matrix to a matrix which presents a smaller coherence than the initial one. Expand
Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization
TLDR
A framework for the joint design and optimization, from a set of training images, of the nonparametric dictionary and the sensing matrix is introduced and it is shown that this joint optimization outperforms both the use of random sensing matrices and those matrices that are optimized independently of the learning of the dictionary. Expand
Signal Recovery in Compressive Sensing via Multiple Sparsifying Bases
TLDR
A customized interior-point method is derived to jointly obtain multiple estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient. Expand
Optimized Projections for Compressed Sensing
  • Michael Elad
  • Computer Science
  • IEEE Transactions on Signal Processing
  • 2007
TLDR
This paper considers the optimization of compressed sensing projections, and targets an average measure of the mutual coherence of the effective dictionary, and shows that this leads to better CS reconstruction performance. Expand
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