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RESTORATION OF HDR IMAGES FOR SVE-BASED HDRI VIA A NOVEL DCNN

  • Yilun Xu
  • , Ziyang Liu
  • , Xingming Wu*
  • , Weihai Chen
  • , Zhengguo Li
  • *Corresponding author for this work
  • Beihang University
  • Agency for Science, Technology and Research, Singapore

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

Abstract

Ghosting artifacts are believed to be the Achilles' heel for high dynamic range (HDR) imaging (HDRI) via differently exposed images sequentially captured by a digital device. Spatially varying exposure (SVE)-based HDRI is an efficient solution to prevent the ghosting artifacts from appearing in a HDR image. However, it is challenging to restore a high-quality HDR image with the full resolution from a single raw Bayer image for the SVE-based HDRI. In this paper, a novel deep convolution neural network (DCNN) is proposed to address such a challenging problem. The proposed DCNN includes two distinctive components, a spatially varying convolution and an exposedness-aware compensation branch. The evaluations indicate that the quality of our results significantly surpasses several related algorithms. Related materials will be provided at https://github.com/yilun-xu/SVEHDRI/.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo, ICME 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665438643
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

Keywords

  • deep convolution neural network
  • exposedness-aware compensation branch
  • High-dynamic-range imaging
  • spatially varying convolution
  • spatially varying exposure

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