GHOST-FREE HDR IMAGING VIA UNROLLING LOW-RANK MATRIX COMPLETION

Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee

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

5 Scopus citations

Abstract

We propose a ghost-free high dynamic range (HDR) image synthesis algorithm by unrolling low-rank matrix completion. By exploiting the low-rank structure of the irradiance maps from low dynamic range (LDR) images, we formulate ghost-free HDR imaging as a general low-rank matrix completion problem. Then, we solve the problem iteratively using the augmented Lagrange multiplier (ALM) method. At each iteration, the optimization variables are updated by closed-form solutions and the regularizers are updated by learned deep neural networks. Experimental results show that the proposed algorithm provides better image qualities with fewer visual artifacts compared to state-of-the-art algorithms.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
PublisherIEEE Computer Society
Pages2928-2932
Number of pages5
ISBN (Electronic)9781665441155
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2021-September
ISSN (Print)1522-4880

Conference

Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period19/09/2122/09/21

Keywords

  • High dynamic range imaging
  • Low-rank matrix completion
  • Unrolled optimization

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