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This is a test. The citing facilities is good. I tested it :

Blind deconvolution references:

Superresolution references:

Writing the equations is easy too: \(x=y^{2}\).

\begin{equation} g(t)=\int f_{j}\exp\{-j\omega t\}\\ \end{equation}

Using the macros seems to be more difficult.

\begin{equation} {\textbf{g}}={\textbf{H}}{\widehat{{\textbf{f}}}}+\epsilon,{{\textbf{a}}},{{\textbf{A}}},{\scu{A}},{\widehat{{{\textbf{a}}}}}{\widehat{{{\textbf{A}}}}},{\widetilde{{{\textbf{a}}}}}{\widehat{{{\textbf{A}}}}}\\ \end{equation}

References

  1. O Tichy, V Smidl. Bayesian blind separation and deconvolution of dynamic image sequences using sparsity priors.. IEEE Trans Med Imaging 34, 258-66

  2. SU Park, N Dobigeon, AO Hero. Semi-blind sparse image reconstruction with application to MRFM.. IEEE Trans Image Process 21, 3838-49

  3. T Kenig, Z Kam, A Feuer. Blind image deconvolution using machine learning for three-dimensional microscopy.. IEEE Trans Pattern Anal Mach Intell 32, 2191-204

  4. SD Babacan, J Wang, R Molina, AK Katsaggelos. Bayesian blind deconvolution from differently exposed image pairs.. IEEE Trans Image Process 19, 2874-88

  5. A Tonazzini, I Gerace, F Martinelli. Multichannel blind separation and deconvolution of images for document analysis.. IEEE Trans Image Process 19, 912-25

  6. DG Tzikas, AC Likas, NP Galatsanos. Variational Bayesian sparse kernel-based blind image deconvolution with Student’s-t priors.. IEEE Trans Image Process 18, 753-64

  7. SD Babacan, R Molina, AK Katsaggelos. Variational Bayesian blind deconvolution using a total variation prior.. IEEE Trans Image Process 18, 12-26

  8. R Molina, J Mateos, AK Katsaggelos. Blind deconvolution using a variational approach to parameter, image, and blur estimation.. IEEE Trans Image Process 15, 3715-27

  9. Yusuke Murayama, Ari Ide-Ektessabi. Bayesian image superresolution for hyperspectral image reconstruction. In Computational Imaging X. SPIE, 2012. Link

  10. Tao Wang, Yan Zhang, Yong Sheng Zhang. SuperResolution Image Reconstruction Using a Hybrid Bayesian Approach. 412–419 In Neural Information Processing. Springer Science \(\mathplus\) Business Media, 2006. Link

  11. Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii. Edge-Preserving Bayesian Image Superresolution Based on Compound Markov Random Fields. 611–620 In Artificial Neural Networks ICANN 2007. Springer Science \(\mathplus\) Business Media, 2007. Link

  12. Frédéric Champagnat, Guy Le Besnerais, Caroline Kulcsár. Bayesian Approach in Performance Modeling: Application to Superresolution. 109–139 In Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing. John Wiley & Sons Inc., 2015. Link

  13. Superresolution. 781–781 In Computer Vision. Springer US, 2014. Link

  14. MO Camponez, OT Evandro, M Sarcinelli-Filho. Super-resolution image reconstruction using non-parametric Bayesian INLA approximation.. IEEE Trans Image Process 21, 3491-501

  15. A Kanemura, S Maeda, S Ishii. Superresolution with compound Markov random fields via the variational EM algorithm.. Neural Netw 22, 1025-34

  16. C Cai, T Rodet, S Legoupil, A Mohammad-Djafari. A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography.. Med Phys 40, 111916

  17. XM Li, A Mohammad-Djafari, M Dumitru, S Dulong, E Filipski, S Siffroi-Fernandez, A Mteyrek, F Scaglione, C Guettier, F Delaunay, F Lévi. A circadian clock transcription model for the personalization of cancer chronotherapy.. Cancer Res 73, 7176-88 (2013).

  18. H Ayasso, A Mohammad-Djafari. Joint NDT image restoration and segmentation using Gauss-Markov-Potts prior models and variational Bayesian computation.. IEEE Trans Image Process 19, 2265-77

  19. M Nikolova, J Idier, A Mohammad-Djafari. Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF.. IEEE Trans Image Process 7, 571-85

  20. N Bali, A Mohammad-Djafari. Bayesian approach with hidden Markov modeling and mean field approximation for hyperspectral data analysis.. IEEE Trans Image Process 17, 217-25

  21. MM Ichir, A Mohammad-Djafari. Hidden Markov models for wavelet-based blind source separation.. IEEE Trans Image Process 15, 1887-99

  22. C Soussen, A Mohammad-Djafari. Polygonal and polyhedral contour reconstruction in computed tomography.. IEEE Trans Image Process 13, 1507-23

  23. MK Nguyen, A Mohammad-Djafari. Bayesian approach with the maximum entropy principle in image reconstruction from microwave scattered field data.. IEEE Trans Med Imaging 13, 254-62

  24. A Mohammad-Djafari, G Demoment. Maximum entropy image reconstruction in X-ray and diffraction tomography.. IEEE Trans Med Imaging 7, 345-54

  25. A Mohammad-Djafari, G Demoment. Maximum entropy Fourier synthesis with application to diffraction tomography.. Appl Opt 26, 1745-54

  26. F Héron, A Mohammad-Djafari, M Degeorges, J Perrin. Correlative study of simultaneous surface and intracardiac recordings of electrical activity from the specific atrioventricular conduction pathways.. Eur Heart J 2, 419-27

  27. A Mohammad-Djafari, F Heron, R Duperdu, J Perrin. Noninvasive recording of the his-purkinje system electrical activity by a digital system design.. J Biomed Eng 3, 147-52

  28. Ali Mohammad-Djafari. Introduction to Inverse Problems in Imaging and Vision. 15–58 In Mohammad-Djafari/Inverse Problems in Vision and 3D Tomography. John Wiley & Sons Inc., 2013. Link

  29. Doriano-Boris Pougaza, Ali Mohammad-Djafari, Ali Mohammad-Djafari, Jean-Franccois Bercher, Pierre Bessie're. Maximum Entropies Copulas. AIP, 2011. Link

  30. Inverse Problems in Vision and 3D Tomography. John Wiley & Sons Inc., 2013. Link

  31. Masoud Rabiei, Mohammad Modarres, Ali Mohammad-Djafari, Ali Mohammad-Djafari, Jean-Franccois Bercher, Pierre Bessie're. Bayesian Knowledge Fusion in Prognostics and Health ManagementA Case Study. AIP, 2011. Link

  32. Sha Zhu, Ali Mohammad-Djafari, Ali Mohammad-Djafari, Jean-Franccois Bercher, Pierre Bessie're. A Bayesian approach to Fourier Synthesis inverse problem with application in SAR imaging. AIP, 2011. Link

  33. Boujemaa Ait-El-Fquih, Ali Mohammad-Djafari, Ali Mohammad-Djafari, Jean-Franccois Bercher, Pierre Bessie're. Signals and Images Foreground∕Background Joint Estimation and Separation. AIP, 2011. Link

  34. Ali Mohammad-Djafari. From deterministic to probabilistic approaches to solve inverse problems. In Bayesian Inference for Inverse Problems. SPIE, 1998. Link

  35. Hichem Snoussi, Ali Mohammad-Djafari. Image Separation. 377–410 In Mohammad-Djafari/Inverse Problems in Vision and 3D Tomography. John Wiley & Sons Inc., 2013. Link

  36. Maximum Entropy and Bayesian Methods. Springer Netherlands, 1993. Link

  37. Doriano-Boris Pougaza, Ali Mohammad-Djafari. New copulas obtained by maximizing Tsallis or Re'nyi entropies. AIP, 2012. Link

  38. A. Mohammad-Djafari, K.H. Knuth. Bayesian approaches. 467–513 In Handbook of Blind Source Separation. Elsevier, 2010. Link

  39. Ali Mohammad-Djafari, Nasser Qaddoumi, Reza Zoughi. Blind deconvolution approach for resolution enhancement of near-field microwave images. In Mathematical Modeling Bayesian Estimation, and Inverse Problems. SPIE, 1999. Link