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Spectrum-estimation based lossless information recovery for sparse array patterns
by
Feng, Guorui
, Qian, Zhenxing
, Zhang, Xinpeng
in
Algorithms
/ Arrays
/ Artificial Intelligence
/ Business and Management
/ Communications networks
/ Computer Communication Networks
/ Fourier analysis
/ Fourier transforms
/ Interpolation
/ IT in Business
/ Lossless
/ Mathematical analysis
/ Mathematical models
/ Probability Theory and Stochastic Processes
/ Spectra
/ Studies
/ Telecommunications systems
2012
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Spectrum-estimation based lossless information recovery for sparse array patterns
by
Feng, Guorui
, Qian, Zhenxing
, Zhang, Xinpeng
in
Algorithms
/ Arrays
/ Artificial Intelligence
/ Business and Management
/ Communications networks
/ Computer Communication Networks
/ Fourier analysis
/ Fourier transforms
/ Interpolation
/ IT in Business
/ Lossless
/ Mathematical analysis
/ Mathematical models
/ Probability Theory and Stochastic Processes
/ Spectra
/ Studies
/ Telecommunications systems
2012
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Do you wish to request the book?
Spectrum-estimation based lossless information recovery for sparse array patterns
by
Feng, Guorui
, Qian, Zhenxing
, Zhang, Xinpeng
in
Algorithms
/ Arrays
/ Artificial Intelligence
/ Business and Management
/ Communications networks
/ Computer Communication Networks
/ Fourier analysis
/ Fourier transforms
/ Interpolation
/ IT in Business
/ Lossless
/ Mathematical analysis
/ Mathematical models
/ Probability Theory and Stochastic Processes
/ Spectra
/ Studies
/ Telecommunications systems
2012
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Spectrum-estimation based lossless information recovery for sparse array patterns
Journal Article
Spectrum-estimation based lossless information recovery for sparse array patterns
2012
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Overview
One of the open problems in lossless information hiding research is how to get adaptively better difference image architectures for given applications. In this paper we propose a simple and efficient approach to predict high-similar interpolation image from its sparse pattern and spectral expansion. After difference operator, peak value of the spike is very high. This method also provides a mathematic framework for evaluating de-correlating algorithm and can therefore be used to benchmark new algorithms. Finally, a proper reversible data hiding algorithm is also enclosed which refers conventional difference expansion principle. Overflow and underflow is considered in the fusion way. Simulations results demonstrate and verify that our new approach is much effective than local difference expansion method with good generalization performance.
Publisher
Springer US,Springer Nature B.V
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