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Image cryptosystems based on PottsNICA algorithms. Meng-Hong Chen Jiann-Ming Wu Department of Applied Mathematics National Donghwa University. Blind Source Separation (BSS). Sources. Unknown Mixing Structure. Observations. BSS by PottsICA. PottsNICA. Observations
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Image cryptosystems based on PottsNICA algorithms Meng-Hong Chen Jiann-Ming Wu Department of Applied Mathematics National Donghwa University
Blind Source Separation (BSS) Sources Unknown Mixing Structure Observations
BSS by PottsICA PottsNICA Observations Recovered sources
The ICA problem Unknown mixing structure: Unkown statistical independent sources: S= Observations:
The goal of ICA The goal is to find W to recover independent sources by such that The joint distribution is as close as possible to the product of the marginal distributions
The Kullback-Leibler Divergence The criterion on independency of components of y can be quantified by he Kullback-Leibler divergence
Potts Modeling Partition the range of each output component … …
Energy function for ICA To minimize L’ is to solve a mixed integer and linear programming
Annealed neural dynamics Boltzmann distribution Use mean field equations to find the mean configuration at each
Derivation of mean field equations Free energy by
A hybrid of mean field annealing MFE ( 1 ) ( 2 )
Natural gradient descent method W’W ( 3 ) W’W
Simulations We test the PottsICA method using facial images where the last one is a noise image. The parameters for the PottsICA algorithm are K=10, c₁=8, c₂=2 and η=0.001; the β parameter has an initial value of and each time it is increased to β by the scheduling process. The diagonal and last column of the mixing matrix A are lager than others. As follows,
Figure1 Original images Mixtures of the sources by the mixing matrix A(4x4) Recovered images by PossNICA N = 4
Figure2 N = 5
Figure3 N = 8
TableThe performance of the three algorithms for tests by Amarievaluation