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Low-Light Light-Field Image Enhancement With Geometry Consistency

  • Deyang Liu
  • , Zhengqu Li
  • , Xin Zheng
  • , Jian Ma
  • , Yuming Fang*
  • *Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Low-light Light Field (LF) enhancement aims to recover high-quality LF images from their corresponding low-quality counterparts. Although some progresses has been made, the effective utilization of LF geometry consistency for efficient low-light LF enhancement remains a challenge. Most existing methods do not adequately leverage LF geometry information during the light-up process of low-light LF, leading to a significant performance drop. To relieve this issue, we propose a low-light LF enhancement method by fully considering the geometry consistency. By introducing a spatial-epipolar geometry information interaction model, our method is able to enhance the low-light LF quality by effectively aggregating spatial information and epipolar geometry information from LF images. Moreover, to further generate higher-quality enhancements for low-light images, we design a three-stage network to enhance the fine detail information. Experimental results reveal that our method demonstrates superior low-light light field enhancement capabilities compared to previous approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision
Subtitle of host publication7th Chinese Conference, PRCV 2024, Urumqi, China, October 18–20, 2024, Proceedings, Part VIII
EditorsZhouchen Lin, Ming-Ming Cheng, Ran He
Place of PublicationSingapore
PublisherSpringer 
Pages455-467
ISBN (Electronic)978-981-97-8685-5
ISBN (Print)9789819786848
DOIs
Publication statusPublished - 2025
Externally publishedYes
Event7th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2024) - Urumqi, China
Duration: 18 Oct 202420 Oct 2024
http://www.prcv.cn/

Publication series

NameLecture Notes in Computer Science
Volume15038
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2024)
PlaceChina
CityUrumqi
Period18/10/2420/10/24
Internet address

Research Keywords

  • Geometry consistency
  • Image enhancement
  • Low-light light field

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