@inproceedings{c06fe8e1255244e3812ca3a877eab33d,
title = "Moir{\'e} Artifacts Removal in Screen-shot Images via Multiple Domain Learning",
abstract = "We propose a deep learning-based moir{\'e} artifacts removal algorithm for screen-shot images using multiple domain learning. First, we develop the pixel and discrete cosine transform (DCT) networks to estimate clean preliminary images by exploiting complementary information of the moir{\'e} artifacts in different domains. Next, we develop a clean edge predictor to estimate a clean edge map for the input moir{\'e} image. Then, we propose the refinement network to further improve the quality of the pixel and DCT outputs using the estimated edge map as the guide information and to merge the two refined results to provide the final result. Experimental results on a public dataset show that the proposed algorithm outperforms conventional algorithms in quantitative and qualitative comparison.",
author = "Vien, \{An Gia\} and Hyunkook Park and Chul Lee",
note = "Publisher Copyright: {\textcopyright} 2020 APSIPA.; 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 ; Conference date: 07-12-2020 Through 10-12-2020",
year = "2020",
month = dec,
day = "7",
language = "English",
series = "2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1268--1273",
booktitle = "2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings",
address = "United States",
}