HISTOGRAM-BASED TRANSFORMATION FUNCTION ESTIMATION FOR LOW-LIGHT IMAGE ENHANCEMENT

Jaemin Park, An Gia Vien, Jin Hwan Kim, Chul Lee

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

12 Scopus citations

Abstract

We propose a learning-based low-light image enhancement algorithm, called the histogram-based transformation function estimation network (HTFNet), that estimates transformation functions using the histogram of an input image. First, we obtain an attention image that indicates the pixel-wise information on the level of enhancement. Then, the proposed HTFNet generates the transformation functions by exploiting both the spatial and statistical information of the input image by combining two feature maps extracted from the input image and its histogram. Finally, the enhanced images are obtained via channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality compared with the state-of-the-art algorithms.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PublisherIEEE Computer Society
Pages1-5
Number of pages5
ISBN (Electronic)9781665496209
DOIs
StatePublished - 2022
Event29th IEEE International Conference on Image Processing, ICIP 2022 - Bordeaux, France
Duration: 16 Oct 202219 Oct 2022

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference29th IEEE International Conference on Image Processing, ICIP 2022
Country/TerritoryFrance
CityBordeaux
Period16/10/2219/10/22

Keywords

  • Low-light image enhancement
  • histogram equalization
  • transformation function

Fingerprint

Dive into the research topics of 'HISTOGRAM-BASED TRANSFORMATION FUNCTION ESTIMATION FOR LOW-LIGHT IMAGE ENHANCEMENT'. Together they form a unique fingerprint.

Cite this