Blind image separation based on reorganization of block DCT

Blind image separation consists in processing a set of observed mixed images to separate them into a set of original components. Most of the current blind separation methods assume that the sources are as statistically independent or sparsity as possible given the observations. However, these hypotheses do not hold in real world situation. Considering that the images do not satisfy the independent and sparsity conditions, so the mixed images cannot be separated with independent component analysis and sparse component analysis directly. In this paper, a method based on reorganization of blocked discrete cosine transform (RBDCT) is first proposed to separate the mixed images. Firstly, we get the sparse blocks through RBDCT, and then select the sparsest block adaptively by linear strength in which the mixing matrix can be estimated by clustering methods. In addition, a theoretical result about the linearity of the RBDCT is proved. The effectiveness of the proposed approach is demonstrated by several numerical experiments and compared the results with other classical blind image methods.

[1]  Terrence J. Sejnowski,et al.  Independent Vector Analysis for Source Separation Using a Mixture of Gaussians Prior , 2010, Neural Computation.

[2]  Swathi Karri,et al.  Steganographic algorithm based on randomization of DCT kernel , 2014, Multimedia Tools and Applications.

[3]  Michael Zibulevsky,et al.  Underdetermined blind source separation using sparse representations , 2001, Signal Process..

[4]  Zhigang Luo,et al.  Online Nonnegative Matrix Factorization With Robust Stochastic Approximation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Zhaoshui He,et al.  Sparse representation and blind source separation of ill-posed mixtures , 2006, Science in China Series F: Information Sciences.

[6]  Andrzej Cichocki,et al.  Estimation of Sparse Nonnegative Sources from Noisy Overcomplete Mixtures Using MAP , 2009, Neural Computation.

[7]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[8]  Yong Xiang,et al.  Nonnegative Blind Source Separation by Sparse Component Analysis Based on Determinant Measure , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Yong Xiang,et al.  Time-Frequency Approach to Underdetermined Blind Source Separation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[10]  Z. Xiong,et al.  A DCT-based embedded image coder , 1996, IEEE Signal Processing Letters.

[11]  Mohamed-Jalal Fadili,et al.  Image Decomposition and Separation Using Sparse Representations: An Overview , 2010, Proceedings of the IEEE.

[12]  Hu Dan,et al.  A new blind image source separation algorithm based on feedback sparse component analysis , 2013 .

[13]  V. G. Reju,et al.  An algorithm for mixing matrix estimation in instantaneous blind source separation , 2009, Signal Process..

[14]  Tülay Adali,et al.  Independent Component Analysis by Entropy Bound Minimization , 2010, IEEE Transactions on Signal Processing.

[15]  Emna Karray,et al.  Blind source separation of hyperspectral images in DCT-domain , 2010, 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop.

[16]  Xue Chen,et al.  Building Association Link Network for Semantic Link on Web Resources , 2011, IEEE Transactions on Automation Science and Engineering.

[17]  Anna Tonazzini,et al.  Restoration of recto–verso colour documents using correlated component analysis , 2013, EURASIP J. Adv. Signal Process..

[18]  Chang Dong Yoo,et al.  Underdetermined Blind Source Separation Based on Subspace Representation , 2009, IEEE Transactions on Signal Processing.

[19]  Wen Gao,et al.  Morphological representation of DCT coefficients for image compression , 2002, IEEE Trans. Circuits Syst. Video Technol..

[20]  Xie Shengli,et al.  Sparse representation and blind source separation of ill-posed mixtures , 2006 .

[21]  Lei Zhang,et al.  Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling , 2013, IEEE Transactions on Image Processing.

[22]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[23]  Lan Chen,et al.  Semantic Link Network-Based Model for Organizing Multimedia Big Data , 2014, IEEE Transactions on Emerging Topics in Computing.

[24]  Hamed Modaghegh,et al.  A new fast and efficient active steganalysis based on combined geometrical blind source separation , 2014, Multimedia Tools and Applications.

[25]  Ercan E. Kuruoglu,et al.  Astrophysical image separation by blind time-frequency source separation methods , 2009, Digit. Signal Process..

[26]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[27]  Lan Chen,et al.  Semantic based representing and organizing surveillance big data using video structural description technology , 2015, J. Syst. Softw..

[28]  Dan Hu,et al.  A new blind image source separation algorithm based on feedback sparse component analysis , 2013, Signal Process..

[29]  Pierre Comon,et al.  Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast With Algebraic Optimal Step Size , 2010, IEEE Transactions on Neural Networks.

[30]  Michael Elad,et al.  L1-L2 Optimization in Signal and Image Processing , 2010, IEEE Signal Processing Magazine.

[31]  Jianqin Zhou,et al.  On discrete cosine transform , 2011, ArXiv.

[32]  Ercan E. Kuruoglu,et al.  Image separation using particle filters , 2007, Digit. Signal Process..

[33]  Ali Sadr,et al.  Security in the speech cryptosystem based on blind sources separation , 2014, Multimedia Tools and Applications.

[34]  Daniel Pérez Palomar,et al.  Quaternion ICA From Second-Order Statistics , 2011, IEEE Transactions on Signal Processing.

[35]  Fabian J. Theis,et al.  Sparse component analysis and blind source separation of underdetermined mixtures , 2005, IEEE Transactions on Neural Networks.

[36]  Yong Xiang,et al.  Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources , 2013, IEEE Transactions on Neural Networks and Learning Systems.