Compressed Sensing System with Simultaneous Optimization of Sensing Matrix and Sparsifying Dictionary Applied in Gastrointestinal Images for Remote Diagnosis

This paper copes with the compressed sensing (CS) technique which can be applied in remote diagnose on gastrointestinal disease. The images from gastrointestinal endoscopes can be compressed by a designed sensing matrix so that the transmission rate can be improved. Then the estimate images can be recovered in the designed sensing matrix and trained dictionary by using proper recovery algorithms. The algorithms are designed for optimizing sensing matrix and sparsifying dictionary alternatively, which will bring accurate recovery performance. The simulations demonstrate the superiority in the application of remote diagnose by adopting CS technique.

[1]  Guillermo Sapiro,et al.  Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization , 2009, IEEE Transactions on Image Processing.

[2]  Gang Li,et al.  Alternating Optimization of Sensing Matrix and Sparsifying Dictionary for Compressed Sensing , 2015, IEEE Transactions on Signal Processing.

[3]  Jiajun Ding,et al.  On Collaborative Compressive Sensing Systems: The Framework, Design and Algorithm , 2017, SIAM J. Imaging Sci..

[4]  Nicolae Cleju,et al.  Optimized projections for compressed sensing via rank-constrained nearest correlation matrix , 2013, ArXiv.

[5]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Miguel R. D. Rodrigues,et al.  On the Use of Unit-Norm Tight Frames to Improve the Average MSE Performance in Compressive Sensing Applications , 2012, IEEE Signal Processing Letters.

[7]  Zhihui Zhu,et al.  On Projection Matrix Optimization for Compressive Sensing Systems , 2013, IEEE Transactions on Signal Processing.

[8]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[9]  Zhihui Zhu,et al.  Approximating Sampled Sinusoids and Multiband Signals Using Multiband Modulated DPSS Dictionaries , 2015, Journal of Fourier Analysis and Applications.

[10]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[11]  Sheng Li,et al.  Sensing Matrix Optimization Based on Equiangular Tight Frames With Consideration of Sparse Representation Error , 2016, IEEE Transactions on Multimedia.

[12]  Rodrigo C. de Lamare,et al.  Gradient-based algorithm for designing sensing matrix considering real mutual coherence for compressed sensing systems , 2017, IET Signal Process..

[13]  Yukihiro Nishida,et al.  A method of estimating coding PSNR using quantized DCT coefficients , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[15]  Mike E. Davies,et al.  Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance , 2010, IEEE Journal of Selected Topics in Signal Processing.

[16]  Rodrigo C. de Lamare,et al.  Joint sensing matrix and sparsifying dictionary optimization applied in real image for compressed sensing , 2017, 2017 22nd International Conference on Digital Signal Processing (DSP).

[17]  Pierre Vandergheynst,et al.  Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes , 2011, IEEE Transactions on Biomedical Engineering.

[18]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[19]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[20]  Tzyy-Ping Jung,et al.  Compressed Sensing of EEG for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware , 2012, IEEE Transactions on Biomedical Engineering.