Blind Image Extraction by Using Local Smooth Information

This paper proposes a new approach to blind image extraction. By using the property that most of near pixels are smooth, blind image extraction is formulated to a generalized eigen-decomposition problem. The key point of our method is the formulation of matrix pencil. Since the image is two dimensional, the values of a small patch are smooth. Based on this observation, two mixed signals are formulated in columnwise order or in rowwise order respectively. The matrix pencil is constructed by using these two mixed signals. The separation weight vector can be obtained by generalized eigen-decomposition. Compared with the $'$Non-Negative ICA$'$ algorithm, the original signals in our algorithm are not required to be well-grounded, which means that they have a non-zero pdf in the region of zeros. In contrast to many second order methods in recent literatures, the two dimensional signals are used. Simulation results on mixed images are employed to further illustrate the advantages of our approach.

[1]  James V. Stone.  Blind Source Separation Using Temporal Predictability , 2001, Neural Computation.

[2]  Andrzej Cichocki,et al.  Adaptive blind signal and image processing , 2002 .

[3]  Hai-Lin Liu,et al.  On blind source separation using generalized eigenvalues with a new metric , 2008, Neurocomputing.

[4]  Mark D. Plumbley.  Lie Group Methods for Optimization with Orthogonality Constraints , 2004, ICA.

[5]  Erkki Oja,et al.  A "nonnegative PCA" algorithm for independent component analysis , 2004, IEEE Transactions on Neural Networks.

[6]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[7]  Mao Ye,et al.  An Efficient Measure of Signal Temporal Predictability for Blind Source Separation , 2007, Neural Processing Letters.

[8]  André Quinquis,et al.  A New Method for Estimating the Number of Harmonic Components in Noise with Application in High Resolution Radar , 2004, EURASIP J. Adv. Signal Process..

[9]  Pierre Comon.  Independent component analysis - a new concept? signal processing , 1994 .

[10]  Mao Ye,et al.  Global convergence analysis of a discrete time nonnegative ICA algorithm , 2006, IEEE Transactions on Neural Networks.

[11]  Juha Karhunen,et al.  Neural networks for blind separation with unknown number of sources , 1999, Neurocomputing.

[12]  Zhaoshui He,et al.  A Note on Stone's Conjecture of Blind Signal Separation , 2005, Neural Computation.

[13]  Gene H. Golub,et al.  Matrix computations , 1983 .

[14]  Erkki Oja,et al.  Blind Separation of Positive Sources by Globally Convergent Gradient Search , 2004, Neural Computation.

[15]  Seungjin Choi,et al.  Blind separation of second-order nonstationary and temporally colored sources , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[16]  Erkki Oja,et al.  Independent Component Analysis , 2001 .