Full list of publications and biography can be downloaded from here. List of publications indexed by Croatian Scientific Bibliography (CROSBI) can be accessed on the following link: Ivica Kopriva@CROSBI.






1. W. Wasylkiwskyj, I. Kopriva, A.I. Zaghloul, H. Abdallah and M. Doroslovacki, “System for passive Estimation of Range and Bearing of RF Emitters,” NSA/NSF project, 05/2002-12/2004.

2. I. Kopriva, “Infrared image restoration under out-of-focus and low light level conditions,” International Technology Center, Raleigh, NC, 03/2005-05/2005.




1. H. Szu, J. Buss, and I. Kopriva, Nonlinear blind demixing of single pixel underlying radiation sources and digital spectrum local thermometer, US Patent 7,366,564 B2. (PDF)

2. I. Kopriva, Method for real time tumour visualisation and demarcation by means of photodynamic diagnosis, WO 2008/132522 A1.

3. I. Kopriva, I. Jerić, and V. Smrečki, Method of and system for blind extraction of more than two pure components out of spectroscopic or spectrometric measurements of only two mixtures by means of sparse component analysis, PCT /HR2008/000037.

4. I. Kopriva, I. Jerić, Method of and system for blind extraction of more pure components than mixtures in 1D and 2D NMR spectroscopy and mass spectrometry combining sparse component analysis and single component points, PCT/HR2009/000028.

5. I. Kopriva, I. Jerić, Method of and system for blind extraction of more pure components than mixtures in 1D and 2D NMR spectroscopy and mass spectrometry combining sparse component analysis and single component points, US Patent 8,165,373, 24. 4. 2012. (PDF)

6. I. Kopriva, Method for real time tumour visualisation and demarcation by means of photodynamic diagnosis, US Patent 8,224,427, 17. 7. 2012. (PDF)

7. I. Kopriva, M. Popović Hadžija. M. Hadžija. G. Aralica, Method and Apparatus for Unsupervised Segmentation of Microscopic Color Image of Unstained Specimen and Digital Staining of Segmented Histological Structures, European Patent 2 921 990 B1 (6. 7. 2016).




T.-M. Huang, V. Kecman, I. Kopriva, "Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised and Unsupervised Learning," Springer Series: Studies in Computational Intelligence, Vol. 17, XVI, ISBN: 3-540-31681-7, 2006.

(Official web page, Some information from Amazon)




Overall impact factor according to 2002-2008 SCI database is approximately 53.1.





  • M. Brbić, I. Kopriva (2018), "Multi-view Low-rank Sparse Subspace Clustering," Pattern Recognition, vol. 73, January 2018, pp. 247-268,, (IF: 4.582) - Q1 (leading 11% in Computer Science, Artificial Intelligence).(Paper) (Code)


  • I. Kopriva, M. Brbić, D. Tolić, N. Antulov-Fantulin, X. Chen, "Fast Clustering in Linear 1D Subspaces: Segmentation of Microscopic Image of Unstained Specimen," SPIE Medical Imaging Symposium 2017 - Digital Pathology Conference, vol. 10140,, editor Metin N. Gurcan, John E. Tomaszewski, Orlando, US, February 11 - 16, 2017. (Paper)


  • I. Kopriva, M. Popović Hadžija, M. Hadžija, G. Aralica, "Offset-Sparsity Decomposition for Enhancement of Color Microscopic Image of Stained Specimen in Histopathology: Further Results," Proceedings of the SPIE Medical Imaging Symposium 2016 - Digital Pathology Conference, vol. 9791, 979124-1 to 8, doi: 10.1117/12.2212198. editor Metin N. Gurcan, San Diego, US, February 27 - March 3, 2016. (Paper)

  • I. Kopriva, F. Shi, X. Chen, "Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography,"  Journal of Biomedical Optics 21(7) 076008 DOI: 10.1117/1.JBO.21.7.076008, IF: 2.56, Q1 in Optics (Paper) .
  • I. Kopriva, W. Ju, B. Zhang, F. Shi, D. Xiang, K. Yu, X. Wang, U. Bagci and X. Chen (2016), "Single-channel Sparse Nonnegative Blind Source Separation Method for Automatic 3D Delineation of Lung Tumor in PET Images," IEEE Journal of Biomedical and Health Informatics, doi:10.1109/JBHI.2016.2624798 (IF: 2.09), Q1 in Computer Science (Information Systems) (Paper).


  • W. Ju, D Xiang, I. Kopriva, B. Zhang, and X. Chen, " Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images with 3D Derivative Features," IEEE Transactions on Image Processing, vol. 24 (12), pp. 5854-5867 (IF: 3.625)

  • I. Kopriva, I. Jerić, L. Brkljačić (2015). Explicit-Implicit Mapping Approach to Nonlinear Blind Separation of Sparse Nonnegative Dependent Sources from a Single Mixture: Pure Components Extraction from Nonlinear Mixture Mass Spectra.  Journal of Chemometrics ,vol. 29(11), pp. 615-626 (IF: 1.5). (Paper) (Supporting Information) (Matlab code and data)

  • I. Kopriva, M. Popović Hadžija, M. Hadžija, G. Aralica (2015). Offset-sparsity decomposition for automated enhancement of color microscopic images of stained specimen in histopathology. Journal of Biomedical Optics 20 doi: 10.1117/1.JBO.20.7.076012 (15 pages) (IF: 2.86) .(PDF) (Matlab code and data)

  • A. G. Savić, S. Živković, K. K. Jovanović, L. Duponchel, I. Kopriva (2015). Complete determination of plant tissues and advanced image analysis - study of needles and stamen. Journal of Chemometrics, vol. 29(10), pp. 521-527, doi: 10.1002/cem/2735 (IF: 1.5) . (Matlab code)
  • I. Kopriva, M. Popović Hadžija, M. Hadžija, G. Aralica (2015). Unsupervised segmentation of low-contrast multichannel images: discrimination of tissue components in microscopic image of unstained specimen,  Scientific Reports 5: 11576, DOI: 10.1038/srep11576 (IF: 5.58) (Paper) (Supplement) (Code and data). The method is pending for patent protection in the US, Canada and EU.
  • I. Kopriva, S. Kapitanović, T. Čačev, "Nonlinear Sparse Component Analysis with a Reference: Variable Selection in Genomics and Proteomics," 12th International Conference on Latent  Variable Analysis and Signal Separation, Lecture Notes in Computer Science 9237, pp. 168-175, DOI: 10.1007/978-3-319-22482-4_19, Liberec, Czech Republic, August 25-28, 2015. (PDF) (ZIP)
  • I. Kopriva, D. Nuzillard, "A Steerable Filter Bank Approach to Endmembers Estimation in Imaging Spectroscopy," accepted for 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), Milan, Italy, July 26-31, 2015. (PDF)
  • I. Kopriva, "A Nonlinear Mixture Model Based Unsupervised Variable Selection in Genomics and Proteomics," accepted for Bioinformatics 2015 - 6th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Lisbon, Portugal, January 12-15, 2015. (PDF)(ZIP)
  • I. Jerić, L. Brkljačić, I. Kopriva (2015). Computationally Efficient Separation of Large Number of Analytes from Small Number of Mixture Mass Spectra. Euroanalysis 2015 - 18th edition of EuroAnalysis the European Conference on Analytical Chemistry, Bordeaux, France, September 6-10 (PDF).




  • I. Kopriva, I. Jerić, (2014). Blind separation of analytes in nuclear magnetic resonance spectroscopy: Improved model for nonnegative matrix factorization. Chemometrics and Intelligent Laboratory Systems, vol. 137, pp.47-56 (IF: 2.29) (PDF) (ZIP)
  • I. Kopriva, I. Jerić, M. Filipović, L. Brkljačić (2014). Empirical Kernel Map Approach to Nonlinear Underdetermined Blind Separation of Sparse Nonnegative Dependent Sources: Pure Components Extraction from Nonlinear Mixtures Mass Spectra.J. of Chemometrics, vol. 28, pp. 704-715 (IF: 1.83) (PDF) (Supporting Information) (ZIP). The method is pending for patent protection in the US.
  • I. Kopriva, J.-P. Royer, N. Thirion-Moreau, P. Comon, (2014). Error anlaysis of low-rank three-way tensor factorization approach to blind source separation. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. (PDF)



  • I. Kopriva, I. Jerić, L. Brkljačić, (2013). Nonlinear Mixture-wise Expansion Approach to Underdetermined Blind Separation of Nonnegative Dependent Sources. Journal of Chemometrics, vol. 27, pp.189-197 (IF: 1.92) (PDF) (Supplement) (ZIP)
  • A. Jukić, I. Kopriva, A. Cichocki, "Tensor decomposition-based feature extraction for noninvasive diagnosis of melanoma from the clinical color image", Biomedical Signal Processing and Control, vol. 8 (6), pp. 755-763, 2013. (IF: 1.07) (PDF)
  • A. Jukić, I. Kopriva, A. Cichocki, "Canonical polyadic decomposition for unsupervised linear feature extraction from protein profiles", accepted for 21st European Signal Processing Conference, Marrakech, Morocco, September 9-13, 2013. (PDF)
  • I. Kopriva, A. Jukić, X. Chen, "Sparseness Constrained Nonnegative Matrix Factorization for Unsupervised 3D Segmentation of Multichannel Images: Demonstration on Multispectral Magnetic Resonance Image of the Brain," SPIE Medical Imaging Symposium, Orlando, FL, February 9-14, 2013. (PDF)



  • M. Filipović, I. Kopriva, A. Cichocki, "Inpainting Color Images in Learned Dictionary," European Signal Processing Conference (EUSIPCO 2012) - special session on Tensor Decompositions and Source Separation, 27-31, 2012, August, Bucharest, Romania. (PDF)



  • I. Kopriva, M. Filipović (2011). A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels, BMC Bioinformatics 2011, 12:496 (IF:3.03). (PDF)
  • A. Radman, M. Gredičak, I. Kopriva, I. Jerić (2011). Predicting antitumor activity of compounds by consensus of regression models trained on a small sample size data, International Journal of Molecular Sciences, vol. 12, 8415-8430 (IF: 2.279). (PDF)
  • M. Filipović, I. Kopriva (2011). A Comparison of Dictionary Based Approaches to Inpainting and Denoising with an Emphasis to Independent Component Analysis Learned Dictionaries, Inverse Problems and Imaging, vol. 5, No. 4, pp. 815-841 (IF=1.403). (PDF, MATLAB)
  • I. Kopriva, M. Hadžija, M. Popović-Hadžija, M. Korolija, A. Cichocki (2011). Rational Variety Mapping for Contrast-Enhanced Nonlinear Unsupervised Segmentation of Multispectral Images of Unstained Specimen, The American Journal of Pathology, vol. 179, No. 2, pp. 547-553 (IF: 5.224). (PDF)
  • I. Kopriva, Q. Du (2011). Tensor Factorization and Continuous Wavelet Transform for Model-free Single-Frame Blind Image Deconvolution, 7th International Symposium on Signal and Image Processing and Analysis, Dubrovnik, Croatia, September 4-6. (PDF)
  • I. Kopriva, A. Jukić, A. Cichocki (2011). Feature extraction for cancer prediction by tensor decomposition of 1D protein expression levels, IASTED Conference on Computational Bioscience CompBio 2011, Cambridge, UK, July 11-13. (PDF)
  • I. Kopriva, X. Chen, Y. Jao (2011). Nonlinear Band Expansion and Nonnegative Matrix Underapproximation for Unsupervised Segmentation of a Liver from a Multi-phase CT image, SPIE Medical Imaging-Image Processing, Orlando, FL, USA, February 12-17, Proc. SPIE Vol. 7962.(PDF)



  • I. Kopriva (2010).  Tensor Factorization for model-free space-variant blind deconvolution of the single- and multi-frame multi-spectral Image, Optics Express, vol. 18, No. 17, pp. 17819-17833 (IF 3.278).(PDF)
  • I. Kopriva, A. Cichocki (2010). Nonlinear Band Expansion and 3D Nonnegative Tensor Factorization for Blind Decomposition of Magnetic Resonance Image of the Brain. Proceedings of 9th International Conference on Latent Variable Analysis and Signal Separation, Lecture Notes Computer Science 6365, pp. 490-497, V. Vigneron (editor), September 27-30, 2010, Saint Malo, France.(PDF)
  • I. Kopriva, A. Peršin, N. Puizina-Ivić, L. Mirić (2010). Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence image, Journal Photochemistry and Photobiology B: Biology, vol. 100, pp. 10-18 (IF: 1.838).(PDF)
  • I. Kopriva, I. Jerić (2010). Blind separation of analytes in nuclear magnetic resonance spectroscopy and mass spectrometry: sparseness-based robust multicomponent analysis, Analytical Chemistry, vol. 82, pp. 1911-1920 (IF: 5.71).(PDF)



  • I. Kopriva, A. Cichocki (2009).Blind decomposition of low-dimensional multi-spectral image by sparse component analysis, Journal of Chemometrics, vol. 23, issue 11, pp. 590-597(IF: 1.415).(PDF)
  • I. Kopriva, A. Cichocki (2009). Blind Multi-spectral Image Decomposition by 3D Nonnegative Tensor Factorization, Optics Letters vol. 34, No. 14, pp 2210-2212(IF: 3.77). (PDF)
  • I. Kopriva (2009). Dependent component analysis for blind restoration of images degraded by turbulent atmosphere, Neurocomputing 72, 2682-2692, (IF: 1.415). (PDF)
  • I. Kopriva, I. Jerić, A. Cichocki (2009). Blind Decomposition of Infrared Spectra Using Flexible Component Analysis," Chemometrics and Intelligent Laboratory Systems 97 (2009) 170-178 (IF: 1.94). (PDF)
  • I. Kopriva, I. Jerić (2009). Multi-component Analysis: Blind Extraction of Pure Components Mass Spectra using Sparse Component Analysis, Journal of Mass Spectrometry, vol. 44, issue 9, pp. 1378-1388 (IF: 2.94). (PDF, MATLAB)
  • I. Kopriva, I. Jerić, V. Smrečki (2009). Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: novel solution obtained by sparse component analysis-based blind decomposition, Analytica Chimica Acta, vol. 653, pp. 143-153 (IF: 3.14). (PDF)
  • W. Wasylkiwskyj and I. Kopriva (2009). Second and Fourth Order Statistics -Based Reduced Polynomial Rooting Direction Finding Algorithms, Signal Processing 89, 1050-1060, 2009(IF: 1.256). (PDF)
  • I. Kopriva and A. Peršin (2009). Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation, Medical Image Analysis 13, 507-518 (IF: 3.6). (PDF)
  • I. Kopriva (2009). 3D Tensor Factorization Approach to Single-frame Model-free Blind Image Deconvolution," Optics Letters, vol. 34, Issue 18, pp. 2385-2387(IF: 3.77). (PDF)



  • Q. Du and I. Kopriva (2008). Automated Target Detection and Discrimination Using Constrained Kurtosis Maximization, IEEE Geoscience Remote Sensing Letters, vol. 5, No. 1, pp. 38-42 (IF: 1.83). (PDF)
  • I. Kopriva and D. Seršić (2008). Wavelet Packets Approach to Blind Separation of Statistically Dependent Sources, Neurocomputing, vol. 71, Issues 7-9, pp. 1642-1655 (IF: 1.415). (PDF, MATLAB)



  • W. Wasylkiwskyj, I. Kopriva and M. Doroslovački (2007). Image Frequency Suppression In Frequency-Scanned Direction-Of-Arrival Estimation Systems, IET Proc. Radar, Sonar & Navigation, vol.1, (3), pp. 191-197 (IF: 0.41). (PDF)
  • I. Kopriva, (2007). Approach to Blind Image Deconvolution by Multiscale Subband Decomposition and Independent Component Analysis, Journal Optical Society of America A, Vol. 24, No.4, pp. 973-983 (IF: 2.002). (PDF)
  • W. Wasylkiwskyj, I. Kopriva, M. Doroslovački, and A.I. Zaghloul (2007). A New Root –Based Direction Finding Algorithm, Radio Science 42: RS2S90, doi: 10.1029/2004RS003147. (IF: 0.951). (PDF)
  • I. Kopriva, A. Peršin, H. Zorc, J. Lipozencic, A. Pasic, K. Kostovic, M. Loncaric. (2007). Visualization of basal cell carcinoma by fluorescence diagnosis and independent component analysis, Photodiagnosis and Photodynamic Therapy 4 pp. 190-196. (PDF)



  • H. Abdallah, W. Wasylkiwskyj, I. Kopriva (2006). Equalization of Numerically Calculated Element Radiation Patterns for Root-Based Direction Finding Algorithms, ACES Journal, ISSN 1054-4887, Vol. 21, No. 1, pp. 76-80, March, 2006 (IF: 0.356).(PDF)
  • Q. Du, I. Kopriva and H. Szu (2006). Independent Component Analysis for Hyperspectral Remote Sensing, Optical Engineering, vol. 45, 017008, January 2006 (IF: 0.952). (PDF)
  • I. Kopriva, D.J. Garrood, V. Borjanović (2006). Single Frame Blind Image Deconvolution by Non-negative Sparse Matrix Factorization, Optics Communications, Vol. 266, Issue 2, pp. 456-464 (IF: 1.456). (PDF)



  • H. Szu and I. Kopriva (2005). Unsupervised Learning with Stochastic Gradient, Neurocomputing, Vol. 68 pp. 130-160 (IF: 0.790). (Download)
  • I. Kopriva (2005). Single Frame Multichannel Blind Deconvolution by Non-negative Matrix Factorization with Sparseness Constraint, Optics Letters, Vol. 30, No. 23, pp. 3135-3137 (IF: 3.882). (PDF)



  • Q. Du, I. Kopriva, and H.Szu (2004). Independent Component Analysis for Classifying Multispectral Images with Dimensionality Limitation, International Journal of Information Acquisition, vol. 1, no. 3, pp.201-216, September 2004. (PDF)
  • I. Kopriva, Q. Du, H. Szu and W. Wasylkiwskyj (2004). Independent Component Analysis Approach to Image Sharpening in the Presence of Atmospheric Turbulence, Optics Communications, Vol. 233 (1-3) pp. 7-14 (IF: 1.581). (PDF)



  • H. Szu, P. Chanyagorn, and I. Kopriva. (2002). Sparse Coding Blind Source Separation through Powerline, Neurocomputing, Vol. 48 (1-4) pp. 1015-1020 (IF: 0.62). (PDF)
  • I.Kopriva, H.H.Szu, A.Persin. (2002). Optical Reticle Trackers with Multi-Source Dicrimination Capability By Using Independent Component Analysis, Optics Communications, Vol. 203 (3-6) pp. 197-211 (IF: 1.488). (PDF)



  • H. H. Szu, I. Kopriva. (2001). Artificial Neural Networks for Noisy Image Super-resolution, Optics Communications, Vol. 198 (1-3) pp. 71-81 (IF: 1.488). (PDF)



  • H. H. Szu, I. Kopriva, A. Peršin. (2000).  Independent component analysis approach to resolve the multi-source limitation of the nutating rising-sun reticle based optical trackers, Optics Communications, Vol. 176 (1-3) pp. 77-89 (IF: 1.488). (PDF)



  • I. Kopriva, A. Peršin. (1999). Discrimination of optical sources by use of adaptive blind source separation theory, Applied Optics, Vol. 38, No. 7, pp. 1115-1126 (IF: 1.515).(PDF)
  • K. Ćosić, I. Kopriva, T. Kostić, M. Slamić, M. Volarević. (1999). Design and implementation of a hardware-in-the-loop simulator for a semi-automatic guided missile system, Journal Simulation Practice & Theory, Vol. 7, Issue 2, pp. 107-123 (IF: 0.182).(PDF)



  • K. Ćosić, I. Kopriva, T. Šikić. (1997). The methodology for digital real time simulation of dynamic systems using modern DSPs, Journal Simulation Practice & Theory, Vol. 5, Issue 2, pp. 137-151 (IF: 0.182).(PDF)



  • K. Ćosić, I. Miler, I. Kopriva. (1992). Workstation for Integrated System Design and Development, Simulation, Vol. 58, No. 3, pp. 152-162 (IF: 0.32).(PDF)

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