@inproceedings{6b4617c4cae8478385543f0b2ca74a0e,
title = "PieNet: Personalized Image Enhancement Network",
abstract = "Image enhancement is an inherently subjective process since people have diverse preferences for image aesthetics. However, most enhancement techniques pay less attention to the personalization issue despite its importance. In this paper, we propose the first deep learning approach to personalized image enhancement, which can enhance new images for a new user, by asking him or her to select about 10–20 preferred images from a random set of images. First, we represent various users{\textquoteright} preferences for enhancement as feature vectors in an embedding space, called preference vectors. We construct the embedding space based on metric learning. Then, we develop the personalized image enhancement network (PieNet) to enhance images adaptively using each user{\textquoteright}s preference vector. Experimental results demonstrate that the proposed algorithm is capable of achieving personalization successfully, as well as outperforming conventional general image enhancement algorithms significantly. The source codes and trained models are available at https://github.com/hukim1124/PieNet.",
keywords = "Image enhancement, Metric learning, Personalization",
author = "Kim, {Han Ul} and Koh, {Young Jun} and Kim, {Chang Su}",
note = "Funding Information: This work was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00464) supervised by the IITP (Institute for Information & communications Technology Promotion), in part by the National Research Foundation of Korea (NRF) through the Korea Government (MSIP) under Grant NRF-2018R1A2B3003896, and in part by the research fund of Chungnam National University. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2020",
doi = "10.1007/978-3-030-58577-8_23",
language = "English",
isbn = "9783030585761",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "374--390",
editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",
booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, Proceedings",
address = "Germany",
}