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Ghosting free HDR for dynamic scenes via shift-maps

Published: 18 December 2016 Publication History

Abstract

Given a set of sequential exposures, High Dynamic Range imaging is a popular method for obtaining high-quality images for fairly static scenes. However, this typically suffers from ghosting artifacts for scenes with significant motion. Also, existing techniques cannot handle heavily saturated regions in the sequence. In this paper, we propose an approach that handles both the issues mentioned above. We achieve robustness to motion (both object and camera) and saturation via an energy minimization formulation with spatio-temporal constraints. The proposed approach leverages information from the neighborhood of heavily saturated regions to correct such regions. The experimental results demonstrate the superiority of our method over state-of-the-art techniques for a variety of challenging dynamic scenes.

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Cited By

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  • (2021)Labeled from Unlabeled: Exploiting Unlabeled Data for Few-shot Deep HDR Deghosting2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00484(4873-4883)Online publication date: Jun-2021
  • (2020)High dynamic range image reconstruction using multi-exposure Wavelet HDRCNN2020 International Conference on Machine Vision and Image Processing (MVIP)10.1109/MVIP49855.2020.9116898(1-4)Online publication date: Feb-2020

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Published In

cover image ACM Other conferences
ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing
December 2016
743 pages
ISBN:9781450347532
DOI:10.1145/3009977
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Google Inc.
  • QI: Qualcomm Inc.
  • Tata Consultancy Services
  • NVIDIA
  • MathWorks: The MathWorks, Inc.
  • Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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Author Tags

  1. exposure fusion
  2. high dynamic range imaging

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  • Research-article

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ICVGIP '16
Sponsor:
  • QI
  • MathWorks
  • Microsoft Research

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ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;
Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

View all
  • (2021)Labeled from Unlabeled: Exploiting Unlabeled Data for Few-shot Deep HDR Deghosting2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR46437.2021.00484(4873-4883)Online publication date: Jun-2021
  • (2020)High dynamic range image reconstruction using multi-exposure Wavelet HDRCNN2020 International Conference on Machine Vision and Image Processing (MVIP)10.1109/MVIP49855.2020.9116898(1-4)Online publication date: Feb-2020

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