activity
20162019
most citedSimple Signal Extension Method for Discrete Wavelet Transform

2 citations · 3 across the 3 of their papers we have counts for

collaborators

7 papers

eess.IV2019

Evaluation of 4D Light Field Compression Methods

David Barina, Tomas Chlubna, Marek Solony +2

Light field data records the amount of light at multiple points in space, captured e.g. by an array of cameras or by a light-field camera that uses microlenses. Since the storage a…

cs.CV2017

The Parallel Algorithm for the 2-D Discrete Wavelet Transform

David Barina, Pavel Najman, Petr Kleparnik +2

The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed usin…

eess.SP20172 cited

Simple Signal Extension Method for Discrete Wavelet Transform

David Barina, Pavel Zemcik, Michal Kula

Discrete wavelet transform of finite-length signals must necessarily handle the signal boundaries. The state-of-the-art approaches treat such boundaries in a complicated and inflex…

cs.PF20171 cited

Accelerating Discrete Wavelet Transforms on Parallel Architectures

David Barina, Michal Kula, Michal Matysek +1

The 2-D discrete wavelet transform (DWT) can be found in the heart of many image-processing algorithms. Until recently, several studies have compared the performance of such transf…

cs.CV2017

Accelerating Discrete Wavelet Transforms on GPUs

David Barina, Michal Kula, Michal Matysek +1

The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transfor…

cs.CV2016

Compression Artifacts Removal Using Convolutional Neural Networks

Pavel Svoboda, Michal Hradis, David Barina +1

This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide signif…