Higher order Teager-Kaiser operators for image analysis: Part I - A monocomponent image demodulation

We present in this paper a new narrowband image demodulation method. Our approach is based on the 2D higher order Teager-Kaiser operators (HOTKO). We show that the introduction of higher orders in the Teager-Kaiser operator, improves a lot the demodulation results, in comparison to the Discrete Energy Separation Algorithm (DESA) and the Analytic Image (AI) method. More precisely, for synthetic images, we show that the approximation errors on both the amplitude and the frequency components are much more lower with our proposed demodulation method than the DESA and the AI method. Moreover, it turns out that for the presented real images, the algorithm is so efficient, especially the amplitude counterpart, that it tracks the most important parts in images, and segments the regions of interest. We show how the algorithm could be used in Sonar images for extracting mines'shadows, which is very important for both military and civil applications.

[1]  Iasonas Kokkinos,et al.  Advances in texture analysis: energy dominant component & multiple hypothesis testing , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[2]  Petros Maragos,et al.  Energy separation in signal modulations with application to speech analysis , 1993, IEEE Trans. Signal Process..

[3]  Abdel-Ouahab Boudraa,et al.  Two-dimensional continuous higher-order energy operators , 2005 .

[4]  Petros Maragos,et al.  Image demodulation using multidimensional energy separation , 1995 .

[5]  Alan C. Bovik,et al.  Am-fm image models , 1996 .

[6]  Marios S. Pattichis,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Analyzing Image Structure by Multidimensional Frequency Modulation Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2006 .

[7]  Petros Maragos,et al.  Coupled geometric and texture PDE-based segmentation , 2005, IEEE International Conference on Image Processing 2005.

[8]  Iasonas Kokkinos,et al.  Texture Analysis and Segmentation Using Modulation Features, Generative Models, and Weighted Curve Evolution , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  J. F. Kaiser,et al.  On a simple algorithm to calculate the 'energy' of a signal , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[10]  Joseph P. Havlicek,et al.  Wideband frequency excursions in computed AM-FM image models , 1998, 1998 IEEE Southwest Symposium on Image Analysis and Interpretation (Cat. No.98EX165).

[11]  A. Boudraa,et al.  An imroved image demodulation algorithm based on Teager-Kaiser operator , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[12]  Petros Maragos,et al.  Higher order differential energy operators , 1995, IEEE Signal Processing Letters.

[13]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[14]  Abdel-Ouahab Boudraa,et al.  Higher order Teager-Kaiser operators for image analysis: PART II - a multicomponent image demodulation , 2009, ICIP.

[15]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[16]  Abdel-Ouahab Boudraa,et al.  2D discrete high order energy operators for surface profiling using white light interferometry , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[17]  Marios S. Pattichis,et al.  AM-FM texture segmentation in electron microscopic muscle imaging , 1999, IEEE Transactions on Medical Imaging.

[18]  Sanjit K. Mitra,et al.  Novel nonlinear filter for image enhancement , 1991, Electronic Imaging.

[19]  Iasonas Kokkinos,et al.  Advances in Variational Image Segmentation Using AM-FM Models: Regularized Demodulation and Probabilistic Cue Integration , 2005, VLSM.