Low-light Image Enhancement via Channel-wise Intensity Transformation

Jaemin Park, An Gia Vien, Chul Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2022
EditorsMasayuki Nakajima, Shogo Muramatsu, Jae-Gon Kim, Jing-Ming Guo, Qian Kemao
PublisherSPIE
ISBN (Electronic)9781510653313
DOIs
StatePublished - 2022
Event2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 - Hong Kong, China
Duration: 4 Jan 20226 Jan 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12177
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2022 International Workshop on Advanced Imaging Technology, IWAIT 2022
Country/TerritoryChina
CityHong Kong
Period4/01/226/01/22

Keywords

  • deep learning
  • Low-light image enhancement
  • transformation function estimation

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