压缩感知-英文版
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sparse signal
measurements
nonzero entries
• Random measurements x will work!
CS Signal Recovery
• Reconstruction/decoding: given find
• L2
fast, wrong
Recovery Result
measuremБайду номын сангаасnts”
– integrates sensing, compression, processing – based on new uncertainty principles and concept of incoherency between two bases
Step
• Signal x is -sparse in basis/dictionary – WLOG assume sparse in space domain • Replace samples with few linear projections
COMPRESSIVE SENSING
Reporter：*** Major：*** Student ID：***
1、Introduction
Contents
2、Compressed sensing technology 3、Conclusion
1、Introduction
Compression Sensing(CS),a kind of new image procesing technology,breaks through the conventional Nyquist sampling barrier and realizes an alternate way of sampling.CS theory states that as long as the signal is compressilbe,or it is sparse in a transform domain, people can always find one observation matrix that is not related with the transformation basis to reduce the dimension of the high-dimensional signal through the transformation,and the original signal can then be reconstructed in high probability by solving an optimization problem.
• Compressive sensing
– – – – exploits signal sparsity/compressibility information based on new uncertainty principles integrates sensing, compression, processing natural for sensor network applications
Application
Compressive Sensing:
A New Framework for Signal Processing. Computational
Networked sensing placing increasing pressure on signal/image processing hardware and algs to support. higher resolution / denser sampling »ADCs, cameras, imaging systems, „ + large numbers of sensors »multi-view target data bases, camera arrays + and networks, pattern recognition systems,„ increasing numbers of modalities »acoustic, seismic, RF, visual, IR, SAR, „ =
• Ongoing research
– new kinds of cameras and imaging algorithms – new“analog-to-information” converters (analog CS) – manifold CS for multiple signals/images
Pressure is on Signal Processing
deluge of data
» how to acquire, store, fuse, process efficiently?
2、Compressed sensing technology
Compressive sensing (CS) principle “sparse signal statistics can be recovered from a small number of nonadaptive linear
20k random projections
7k–term wavelet approximation
E. J. Candès and J. Romberg, “Practical Signal Recovery from Random Projections,” 2004.
3、Conclusion
measurements
nonzero entries
• Random measurements x will work!
CS Signal Recovery
• Reconstruction/decoding: given find
• L2
fast, wrong
Recovery Result
measuremБайду номын сангаасnts”
– integrates sensing, compression, processing – based on new uncertainty principles and concept of incoherency between two bases
Step
• Signal x is -sparse in basis/dictionary – WLOG assume sparse in space domain • Replace samples with few linear projections
COMPRESSIVE SENSING
Reporter：*** Major：*** Student ID：***
1、Introduction
Contents
2、Compressed sensing technology 3、Conclusion
1、Introduction
Compression Sensing(CS),a kind of new image procesing technology,breaks through the conventional Nyquist sampling barrier and realizes an alternate way of sampling.CS theory states that as long as the signal is compressilbe,or it is sparse in a transform domain, people can always find one observation matrix that is not related with the transformation basis to reduce the dimension of the high-dimensional signal through the transformation,and the original signal can then be reconstructed in high probability by solving an optimization problem.
• Compressive sensing
– – – – exploits signal sparsity/compressibility information based on new uncertainty principles integrates sensing, compression, processing natural for sensor network applications
Application
Compressive Sensing:
A New Framework for Signal Processing. Computational
Networked sensing placing increasing pressure on signal/image processing hardware and algs to support. higher resolution / denser sampling »ADCs, cameras, imaging systems, „ + large numbers of sensors »multi-view target data bases, camera arrays + and networks, pattern recognition systems,„ increasing numbers of modalities »acoustic, seismic, RF, visual, IR, SAR, „ =
• Ongoing research
– new kinds of cameras and imaging algorithms – new“analog-to-information” converters (analog CS) – manifold CS for multiple signals/images
Pressure is on Signal Processing
deluge of data
» how to acquire, store, fuse, process efficiently?
2、Compressed sensing technology
Compressive sensing (CS) principle “sparse signal statistics can be recovered from a small number of nonadaptive linear
20k random projections
7k–term wavelet approximation
E. J. Candès and J. Romberg, “Practical Signal Recovery from Random Projections,” 2004.
3、Conclusion