no code implementations • 25 Aug 2017 • David Barina, Pavel Najman, Petr Kleparnik, Michal Kula, Pavel Zemcik
Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme.
no code implementations • 18 May 2017 • David Barina, Michal Kula, Michal Matysek, Pavel Zemcik
The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques.
no code implementations • 2 May 2016 • David Barina, Michal Kula, Pavel Zemcik
In this paper, we introduce several new schemes for calculation of discrete wavelet transforms of images.
1 code implementation • 2 May 2016 • Pavel Svoboda, Michal Hradis, David Barina, Pavel Zemcik
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 significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods.
no code implementations • 25 Feb 2016 • Pavel Svoboda, Michal Hradis, Lukas Marsik, Pavel Zemcik
In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks in a situation where the blur kernels are partially constrained.
no code implementations • ICCV 2015 • Roman Juranek, Adam Herout, Marketa Dubska, Pavel Zemcik
Besides that, we collected a new traffic surveillance dataset COD20k which fills certain gaps of the existing datasets and we make it public.