JOURNAL PAPERS BY TOPIC

 

 

Multi-channel Image Restoration

 

  1. N. P. Galatsanos and R. T. Chin, “Digital Restoration of Multi-channel Images,” IEEE Trans. on Acoustics Speech and Signal Processing, Vol. 37, No. 3, pp. 415-421, March 1989.

  1. N. P. Galatsanos, A. K. Katsaggelos, R. T. Chin and A. D. Hillery, “Least Squares Multi-channel Image Restoration, (pdf copy)” IEEE Trans. on Signal Processing, Vol. 39, No. 10, pp. 2222-2236, October 1991.

  1. N. P. Galatsanos and R. T. Chin, “Multi-channel Restoration of Color Images by Kalman Filtering,”IEEE Trans. on Signal Processing, Vol. 39, No. 10, pp. 2237-2252, October 1991.

  2. A. K. Katsaggelos, K. . Lay, and N. P. Galatsanos, “General Framework for Multi-channel Signal Processing,” IEEE Trans. on Image Processing, Vol. 2, No. 3, pp. 417-420, July 1993.

  3. M. Banham, N. P. Galatsanos, H. Gonzalez, and A. K. Katsaggelos, “Multi-channel Restoration of Single-  channel Images Using a Wavelet Based Subband Decomposition,” IEEE Trans. on Image Processing, Vol. 3, No. 6, pp. 821-833, November 1994.

  4. W. Zhu, N. P. Galatsanos and A. K. Katsaggelos, “Regularized Multi-channel Restoration Using Cross-Validation,” Graphical Models and Image Processing, vol. 57, no. 1, pp. 38-54, Jan. 1995.

  5. M. G. Choi, N. P. Galatsanos, and A. K. Katsaggelos, “Multi-channel Regularized Iterative Motion Compensated Restoration of Image Sequences,” Journal of Visual Communications and Image Representation, Vol. 7, No. 3, pp. 244-258, September 1996.

  6. M. G. Choi; Yongyi Yang; N. P. Galatsanos, “Multichannel regularized recovery of compressed video sequences”, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, Volume: 48 Issue:4, April 2001 Page(s): 376 –387.

 

        Image Restoration

  1. N. P. Galatsanos and A. K. Katsaggelos, “Methods for Choosing the Regularization Parameter and Estimating the Noise Variance in Image Restoration and their Relation,” IEEE Trans. on Image Processing, Vol. 1, No. 3, pp. 322-336, July 1992.

  2. Chandas, N. P. Galatsanos, and A. Likas, "Bayesian Restoration Using a New Non-Stationary Edge-Preserving Image Prior", IEEE Trans.   on Image Processing, Vol. 15, No. 10, pp. 2987-2997, October 2006

  3. G. Chantas, N. Galatsanos, A. Likas and M. Saunders, "Variational Bayesian Image Restoration Based on a Product of T-Distributions Image Prior", IEEE Trans. on Image Processing, to appear.

 

        Image Compression   

  1. . Huang, H. M. Dreizen, and N. P. Galatsanos, “Prioritized DCT for Compression and Progressive Transmission of Images,” IEEE Trans. on Image Processing, Vol. 1, No. 4, pp. 477-487, October 1992.

  2. S. Yu and N. P. Galatsanos, “Binary Decomposition for High-Order Entropy Coding of Grayscale Images,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 6, No. 1, pp. 21-31, February 1996.

 

        Image Reconstruction from Compressed   

  1. Y. Yang, N. P. Galatsanos and A. K. Katsaggelos, “Regularized Image Reconstruction to Remove Blocking Artifacts from Block Discrete Cosine Transform Compressed Images,” IEEE Trans. on Circuits and Systems for Video Technology, Vol. 3, No. 6, pp. 421-432, December 1993.

  2. Y. Yang, N. P. Galatsanos and A. K. Katsaggelos, “Projection-Based Spatially-Adaptive Reconstruction of Block Transform Compressed Images,” IEEE Trans. on Image Processing, Vol. 4, No. 7, pp. 896-908, July 1995.

  3. Y. Yang and N. P. Galatsanos, “Compression Artifact Removal Using Projections onto Convex Sets and Line Process Modeling,” IEEE Trans. on Image Processing, Vol. 6, No. 10, pp. 1345-1357, October 1997.

 

         Blind/Partially Known Image Deconvolution

  1. Y. Yang, N. P. Galatsanos, and H. Stark, “Projection Based Blind Deconvolution,” Journal of the Optical Society of America-A, Vol. 11, No. 9, pp. 2401-2409, September 1994.

  2. V. Mesarovic, N. P. Galatsanos, and A. K. Katsaggelos, “Image Restoration Using Regularized Constrained Total-Least Squares,” IEEE Trans. on Image Processing, vol. 4, No. 8, pp. 1096-1108, August 1995.

  3. W. Zhu, Y. Wang, N. P. Galatsanos, and J. Zhang, “An Efficient Solution to the Regularized Total Least Squares Approach for Non-Convolutional Linear Inverse Problems,” IEEE Trans. on Image Processing, Vol. 8, Νο. 11, pp. 1657-1661, November 1999. 

  4. V. N. Mesarovic, N. P. Galatsanos, and M. N. Wernick, “Iterative LMMSE Restoration of Partially-Known Blurs,” Journal of the Optical Society of America-A, Vol. 17, pp. 711-723, April 2000.

  5. N. P. Galatsanos, V. N. Mesarovic, R. M. Molina and A. K. Katsaggelos, “Hierarchical Bayesian Image Restoration from Partially-Known Blurs,” IEEE Trans. on Image Processing, Vol. 9, No. 10, pp. 1784-1797, October 2000.

  6. N. P. Galatsanos, V. N. Mesarovic, R. M. Molina, J. Mateos, and A. K. Katsaggelos, “Hyper-parameter Estimation Using Gamma Hyper-priors in Image Restoration from Partially-Known Blurs,” Optical Engineering,  41(8), pp. 1845-1854, August 2002.

  7. Likas and N. Galatsanos, “A Variational Approach For Bayesian Blind Image Deconvolution”, IEEE Transactions on Signal Processing, Volume: 52 , Issue: 8 , pp:2222 – 2233, August 2004.

         Filter Design

  1. K. C. Haddad, H. Stark and N. P. Galatsanos, “Design of Two-Channel Equi-ripple FIR Linear-Phase Quadrature Mirror Filters Using The Vector Space Projection Method,” IEEE Signal Processing Letters, Vol. 5, No. 167-170, July 1998.
  2. K. Haddad, H. Stark, and N. P. Galatsanos, “Design of Digital Linear-Phase FIR Crossover Systems for Loudspeakers by the Method of Vector Space Projections,” IEEE Trans. on Signal Processing, Vol. 47, No. 11, pp. 3058-3066, November 1999.
  3. K. Haddad, H. Stark, and N. P. Galatsanos, “Constrained FIR Filters Design by the Method of Vector Space Projections,” IEEE Transactions οn Circuits and Systems II Analog and Digital Signal Processing, Vol. 47, No. 8, pp. 714-726, August 2000.

        Image Segmentation

  1. J. Brankov, N. Galatsanos, Y. Yang and M. Wernick, “Segmentation of Dynamic PET or fMRI Images Based on a Similarity Metric”, IEEE Transactions on Nuclear Science, October 2003
  2. K. Blekas, A. Likas, N. Galatsanos and I. E. Lagaris. “A Spatially-Constrained Mixture Model for Image Segmentation”,  IEEE Transactions on Neural Networks, Volume 16,  Issue 2,  March 2005 Page(s):494 - 498
  3. C. Nikou, N. Galatsanos, and A. Likas, "A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation", IEEE Trans. on Image Processing, Vol. 16, No. 4, pp. 1121-1130, April 2007.
  4. K. Blekas, C. Nikou, N. Galatsanos and N. Tsekos, "A regression mixture model with spatial constraints for clustering spatiotemporal data", Journal of Artificial  Intelligence Tools, to appear.

        Template Matching

  1. A. Abu-Naser, N. P. Galatsanos, M. Wernick and D. Shonfeld, “Object Recognition Based on Impulse Restoration Using the Expectation-Maximization Algorithm,” Journal of the Optical Society of America, Vol. 15, No. 9, pp. 2327-2340, September 1998.

  2. R. Dufour, E. Miller and N. P. Galatsanos, “Template matching based object recognition with unknown geometry”, IEEE Transactions  on Image Processing, vol. 11, No. 12, pp. 1385-1396, December 2002.

  3. Abu-Naser, N. P. Galatsanos, and M. N. Wernick, "Methods of Detecting Objects in Photon Limited Images", Journal of the Optical Society of America-A, A 23, 272-278, February 2006.

  4. D. Tzikas, L. Wei, A. Likas, Y. Yang, and N. Galatsanos, "A Tutorial on Relevance Vector Machines for Regression and Classification with Applications", EURASIP NEWS LETTER, Vol. 17, No. 2, pp. 4-23, June 2006.

  5. D. Tzikas, A. Likas, and N. Galatsanos, "Large Scale Multikernel Relevance Vector Machine for Object Detection", International Journal on Artificial Intelligence Tools, to appear.

        Image Super-Resolution

  1. N. A. Woods, N. P. Galatsanos, and A. K. Katsaggelos, "Stochastic Methods for Joint Registration and  Interpolation and Multiple Under Sampled Images", IEEE Trans. on Image Processing, vol. 15, no. 1, pp. 201-213, Jan. 2006.
  2. J. Chantas, N. P. Galatsanos, and N. Woods, "Super Resolution Based on Fast Registration and Maximum A Posteriori Reconstruction", IEEE Trans. on Image Processing, to appear

 

        Medical Imaging and Applications

  1. J. L. Wuster, H. Stark, and N. P. Galatsanos, “Investigation of Smoothing Techniques for Positron Emission Tomography Imaging,” Journal of Electronic Imaging, Vol. 3, No. 2, pp. 164-175, April 1994.

  2. K. Saadah, N. P. Galatsanos, D. Bless and A. Ramos, “Deformation analysis of the vocal folds from video-stroboscopic image sequences,” Journal of the Acoustical Society of America, Vol. 103, No. 6, pp. 3627-3641, June 1998.

  3. I El-Naqa, Y Yang, M N. Wernick, N. P. Galatsanos, and R M. Nishikawa, “A Support Vector Machine Approach for Detection of Microcalcifications”, IEEE Transactions on Medical Imaging, Vol. 21, No. 12, pp. 1552-1653, December 2002.

  4. .M. Wernick, O. Wirjadi, D. Chapman, Z. Zhong, N. Galatsanos, Y. Yang, J. Brankov, O. Oltulu, M.  Anastasio and C. Muehleman, “Multiple-image Radiography”, Physics in Medicine and Biology, Vol. 48, No. 23, pp. 3875-3895, December 2003.

  5. El. Naqa, Y. Yang, and N. Galatsanos and M. Wernick. “A Similarity Learning Approach to Content Based Image Retrieval: Application to Digital Mammography”, IEEE Transactions on Medical Imaging, Volume: 23 , Issue: 10 , pp:1233-1244, Oct. 2004

  6. K. Blekas, N. P. Galatsanos, A. Likas, and I. E. Lagaris, "Mixture Model Analysis of DNA Micro-Array Images", IEEE Transactions on Medical Imaging, Volume: 24, Issue: 7,   pp. 901- 909, July 2005.

  7. A. Lukic, M. Wernick, D. Tzikas, X. Chen, A. Likas, N. Galatsanos, Y. Yang, F. Zhao and S. Strother, “Bayesian Kernel Methods for Analysis of Functional Neuroimages”, IEEE Trans. on Medical Imaging, to appear.

  8. A. A. Tzika, L. Astrakas, H.i Cao, D. Mintzopoulos, O. C. Andronesi, M. Mindrinos, J. Zhang, L. Rahme, K. Blekas, A.C. Likas, N.P. Galatsanos and P. M. Black “Combination of high-resolution magic angle spinning proton magnetic resonance spectroscopy and microscale genomics to type brain tumor biopsies”, International Journal of Molecular Medicine, to appear.

 

         Image Watermarking

  1. P. Dong, J. Brankov, N. Galatsanos, Y. Yang, and F. Davoine, “Digital Watermarking Robust to Geometric Distortion”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2140-2150, December 2005.

  2. A. Mairgiotis, N. Galatsanos and Y. Yang, "New Additive Watermark Detectors Based on a Hierarchical Spatially Adaptive Image Model", IEEE Trans. on Information Forensics and Security, to appear

 

         Bayesian Methods (includes papers from all the above categories)

 

  1. N. P. Galatsanos, V. N. Mesarovic, R. M. Molina and A. K. Katsaggelos, “Hierarchical Bayesian Image Restoration from Partially-Known Blurs,” IEEE Trans. on Image Processing, Vol. 9, No. 10, pp. 1784-1797, October 2000.

  2. N. P. Galatsanos, V. N. Mesarovic, R. M. Molina, J. Mateos, and A. K. Katsaggelos, “Hyper-parameter Estimation Using Gamma Hyper-priors in Image Restoration from Partially-Known Blurs,” Optical Engineering,  41(8), pp. 1845-1854, August 2002.

  3. J. Brankov, N. Galatsanos, Y. Yang and M. Wernick, “Segmentation of Dynamic PET or fMRI Images Based on a Similarity Metric”, IEEE Transactions on Nuclear Science, October 2003

  4. A. Likas and N. Galatsanos, “A Variational Approach For Bayesian Blind Image Deconvolution”, IEEE Transactions on Signal Processing, Volume: 52 , Issue: 8 , pp:2222 – 2233, August 2004.

  5. K. Blekas, A. Likas, N. Galatsanos and I. E. Lagaris. “A Spatially-Constrained Mixture Model for Image Segmentation”,  IEEE Transactions on Neural Networks, Volume 16,  Issue 2,  March 2005 Page(s):494 - 498

  6. J. Chandas, N. P. Galatsanos, and A. Likas, "Bayesian Restoration Using a New Non-Stationary Edge-Preserving Image Prior", IEEE Trans.   on Image Processing, Vol. 15, No. 10, pp. 2987-2997, October 2006

  7. A. Lukic, M. Wernick, D. Tzikas, X. Chen, A. Likas, N. Galatsanos, Y. Yang, F. Zhao and S. Strother, “Bayesian Kernel Methods for Analysis of Functional Neuroimages”, IEEE Trans. on Medical Imaging, to appear.

  8. N. A. Woods, N. P. Galatsanos, and A. K. Katsaggelos, "Stochastic Methods for Joint Registration and  Interpolation and Multiple Under Sampled Images", IEEE Trans. on Image Processing, vol. 15, no. 1, pp. 201-213, Jan. 2006.

  9. J. Chantas, N. P. Galatsanos, and N. Woods, "Super Resolution Based on Fast Registration and Maximum A Posteriori Reconstruction", IEEE Trans. on Image Processing, to appear

  10. C. Nikou, N. Galatsanos, and A. Likas, "A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation", IEEE Trans. on Image Processing, Vol. 16, No. 4, pp. 1121-1130, April 2007.

  11. D. Tzikas, A. Likas and N. Galatsanos, "Life After the EM Algorithm: The Variational Approximation for Bayesian Inference", IEEE Signal Processing Magazine, to appear.

  12. G. Chantas, N. Galatsanos, A. Likas and M. Saunders, "Variational Bayesian Image Restoration Based on a Product of T-Distributions Image Prior", IEEE Trans. on Image Processing, to appear.

  13. K. Blekas, C. Nikou, N. Galatsanos and N. Tsekos, "A regression mixture model with spatial constraints for clustering spatiotemporal data", Journal of Artificial  Intelligence Tools, to appear.