@inproceedings{8703586535064deead327f14d69f722d,
title = "Low-light Image Enhancement via Channel-wise Intensity Transformation",
abstract = "We propose a low-light image enhancement algorithm that learns channel-wise transformation functions. First, we develop a lightweight network, called the transformation function estimation network (TFE-Net), to predict the channel-wise transformation functions. TFE-Net learns to generate the transformation functions by considering both the global and local characteristics of the input image. Then, we obtain enhanced images by performing channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality than conventional algorithms.",
keywords = "deep learning, Low-light image enhancement, transformation function estimation",
author = "Jaemin Park and Vien, {An Gia} and Chul Lee",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",
year = "2022",
doi = "10.1117/12.2624209",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Shogo Muramatsu and Jae-Gon Kim and Jing-Ming Guo and Qian Kemao",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2022",
address = "United States",
}