0462116 20240111140923.520160902235959.9 85005983364 000391891900138 10.1109/EUSIPCO.2016.7760343 5 s. E cav_un_epca*0462058978-0-9928-6266-4723-727BudapestEUSIPCO2016 Variational Bayes Image Reconstruction cav_un_auth*0101209 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Šroubek Filip UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0293859 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Soukup Jindřich UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101238 Zpracování obrazové informace Department of Image Processing ZOI ZOI Department of Image Processing Zitová Barbara UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2016/ZOI/sroubek-0462116.pdf 430KB cav_un_auth*0292734 GA13-29225S GA ČR cav_un_auth*0332878 TA04011392 GA TA ČR CZ We propose a general data acquisition model with volatile random displacement of measured samples. Discrepancies between recorded and true positions of the original data is due to the nature of measured data or the acquisition device itself. A reconstruction method based on the Variational Bayesian inference is proposed, which estimates the original data from samples acquired with the acquisition model, and its relation to Jensen’s inequality is discussed. A model variant of 2D image reconstruction is analyzed in detail. Further, we outline a relation between the proposed method and the classic deconvolution problem, and illustrate superiority of the Variational Bayesian approach in the case of small number of samples. cav_un_auth*0332877 24th European Signal Processing Conference (EUSIPCO) 29.08.2016-02.09.2016 Budapest HU JD 2017 3 PO RVO:67985556 http://hdl.handle.net/11104/0262291 S n.a. Proceedings Paper Engineering Electrical Electronic 2016 85005983364 SCOPUS 000391891900138 WOS pdf 430KB cav_un_epca*0462058 Proc. 2016 24th European Signal Processing Conference (EUSIPCO) 978-0-9928-6266-4 723 727 Budapest EUSIPCO 2016