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}


  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.