@inproceedings{7a335a075dce44b8b3502f456d1dfa4f,
title = "Joint super-resolution and compression artifact reduction based on dual-learning",
abstract = "We propose a novel integrated framework to combine the self-learning super-resolution (SR) with dual-learning noise-reduction (NR) for compressed images. Contrary to existing learning based denoising approach, dual-learning based joint SR and NR is proposed by adding a denoised training set. It makes the proposed framework more suitable for highly compressed noise by referring to closer patch in a training set. Also, it is robust for SR artifacts since the joint framework is designed in such a way that one could learn a process to simultaneously perform NR and SR. Experimental results show that the proposed joint SR and NR framework can achieve higher objective and subjective qualities, compared with individual processing of NR and SR.",
keywords = "Dual-learning, compression artifact reduction, image super resolution, self-learning",
author = "Lee, {Oh Young} and Lee, {Jae Won} and Lee, {Dae Yeol} and Kim, {Jong Ok}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Visual Communication and Image Processing, VCIP 2016 ; Conference date: 27-11-2016 Through 30-11-2016",
year = "2017",
month = jan,
day = "4",
doi = "10.1109/VCIP.2016.7805543",
language = "English",
series = "VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "VCIP 2016 - 30th Anniversary of Visual Communication and Image Processing",
}