Compression Artifacts Removal Using Convolutional Neural Networks [article]

Pavel Svoboda and Michal Hradis and David Barina and Pavel Zemcik
2016 arXiv   pre-print
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. We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric weight initialization. We
more » ... de further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level.
arXiv:1605.00366v1 fatcat:fjsdzrl2rvafrhkzg7273b3lcu