Welcome to Authorea!

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.