Background Proposed Method Experimental Results Conclusion. Recovery of CS Data. In practice: fmÃ1 = ΦmÃnunÃ1 + emÃ1. (4) e â RmÃ1: additive noise.
Recovery of Compressive Video Sensing via Dictionary Learning and Forward Prediction Nasser Eslahi, Ali Aghagolzadeh and Seyed Mehdi Hosseini Andargoli Faculty of Electrical and Computer Engineering Babol University of Technology
7th International Symposium on Telecommunications (IST) Tehran, Sept. 2014
Background Proposed Method Experimental Results Conclusion
Outline
1
Background Basics of Compressive Sensing (CS) Sparse Representation Split Bregman Iteration (SBI) Compressive Video Sensing (CVS)
2
Proposed Method Encoding of frame(s) Recovery of non-key frame(s) Recovery of key frame(s)
3
Experimental Results Experiment 1 Experiment 2
4
Conclusion
N.Eslahi, A. Aghagolzadeh, S. M. H. Andargoli — Recovery of Compressive Video Sensing via Dictionary Learning and Forward Prediction
2
Background Proposed Method Experimental Results Conclusion
Background
N.Eslahi, A. Aghagolzadeh, S. M. H. Andargoli — Recovery of Compressive Video Sensing via Dictionary Learning and Forward Prediction
3
Background Proposed Method Experimental Results Conclusion
Basics of Compressive Sensing (CS)
Compressive Sensing (CS), also called Compressed Sensing, or Compressive Sampling ` and Tao, 2006) (Candes ` Romberg & Tao, 2006) (Candes, (Donoho, 2006)
m×n
fm×1 = Φm×n un×1 √
(1)
Φ∈R : a sensing matrix u ∈ Rn : a real value finite length signal ? √ f ∈ Rm : a finite length observation m n
Underdetermined System of linear Equations (USLE)
N.Eslahi, A. Aghagolzadeh, S. M. H. Andargoli — Recovery of Compressive Video Sensing via Dictionary Learning and Forward Prediction
4
Background Proposed Method Experimental Results Conclusion
Basics of Compressive Sensing (CS)
Compressive Sensing (CS), also called Compressed Sensing, or Compressive Sampling ` and Tao, 2006) (Candes ` Romberg & Tao, 2006) (Candes, (Donoho, 2006)
m×n
fm×1 = Φm×n un×1 √
(1)
Φ∈R : a sensing matrix u ∈ Rn : a real value finite length signal ? √ f ∈ Rm : a finite length observation m n
Underdetermined System of linear Equations (USLE)
N.Eslahi, A. Aghagolzadeh, S. M. H. Andargoli — Recovery of Compressive Video Sensing via Dictionary Learning and Forward Prediction
4
Background Proposed Method Experimental Results Conclusion
Basics of Compressive Sensing (CS)
Compressive Sensing (CS), also called Compressed Sensing, or Compressive Sampling ` and Tao, 2006) (Candes ` Romberg & Tao, 2006) (Candes, (Donoho, 2006)
m×n
fm×1 = Φm×n un×1 √
(1)
Φ∈R : a sensing matrix u ∈ Rn : a real value finite length signal ? √ f ∈ Rm : a finite length observation m n
Underdetermined System of linear Equations (USLE)
N.Eslahi, A. Aghagolzadeh, S. M. H. Andargoli — Recovery of Compressive Video Sensing via Dictionary Learning and Forward Prediction
4
Background Proposed Method Experimental Results Conclusion
Sparse Representation
Definition u would be called s-sparse if only its s coefficients in the set of transform domain (ϑ = Ψu) are nonzero and the other n − s coefficients are zero. s
