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research-article

Blind de-ghosting for automatic multi-exposure compositing

Published: 16 December 2009 Publication History

Abstract

High dynamic range (HDR) image, which faithfully represents the real world scene, needs to be tone mapped to a low dynamic range (LDR) image for display/printing purposes. In this work, we propose a novel method to generate an LDR image of a dynamic scene directly from multi-exposure images, captured using methods like Auto-Exposure Bracketting (AEB). Though there are methods such as [Mertens et al. 2009] for producing such an LDR image, they produce artifacts called 'ghosts' in case of moving objects in the scene. We propose a novel approach to determine the moving objects and eliminate them while compositing. We do not assume any knowledge of camera response function and the exposure settings, a must for existing works like [Gallo et al. 2009]. We show that the resultant LDR images are artifact-free and are faithful representations of the HDR images.

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Supplemental material. (a44-raman.zip)

References

[1]
Gallo, O., Gelfand, N., Chen, W., Tico, M., and Pulli, K. 2009. Artifact-free high dynamic range imaging. In ICCP, IEEE.
[2]
Jacobs, K., Loscos, C., and Ward, G. 2008. Automatic high-dynamic range image generation for dynamic scenes. IEEE Computer Graphics and Applications 28, 2, 84--93.
[3]
Mertens, T., Kautz, J., and Reeth, F. V. 2009. Exposure fusion: A simple and practical alternative to high dynamic range photography. Computer Graphics Forum 28, 1, 161--171.
[4]
Reinhard, E., Ward, G., Pattanaik, S., and Debevec, P. 2005. High Dynamic Range Imaging: Acquisition, Display and Image-Based Lighting. Morgan Kaufmann Publishers.

Cited By

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  • (2024)Joint Denoising and HDR for RAW Image SequencesIEEE Transactions on Computational Imaging10.1109/TCI.2024.335464910(277-290)Online publication date: 2024
  • (2023)IID-MEF: A multi-exposure fusion network based on intrinsic image decompositionInformation Fusion10.1016/j.inffus.2023.02.03195(326-340)Online publication date: Jul-2023
  • (2023)Current advances and future perspectives of image fusion: A comprehensive reviewInformation Fusion10.1016/j.inffus.2022.09.01990(185-217)Online publication date: Feb-2023
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cover image ACM Conferences
SIGGRAPH ASIA '09: ACM SIGGRAPH ASIA 2009 Posters
December 2009
58 pages
ISBN:9781450379342
DOI:10.1145/1666778
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 December 2009

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SA09
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SA09: SIGGRAPH ASIA 2009
December 16 - 19, 2009
Yokohama, Japan

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

View all
  • (2024)Joint Denoising and HDR for RAW Image SequencesIEEE Transactions on Computational Imaging10.1109/TCI.2024.335464910(277-290)Online publication date: 2024
  • (2023)IID-MEF: A multi-exposure fusion network based on intrinsic image decompositionInformation Fusion10.1016/j.inffus.2023.02.03195(326-340)Online publication date: Jul-2023
  • (2023)Current advances and future perspectives of image fusion: A comprehensive reviewInformation Fusion10.1016/j.inffus.2022.09.01990(185-217)Online publication date: Feb-2023
  • (2022)Haar Wavelet-Based Fusion of Multiple Exposure Images for High Dynamic Range ImagingSN Computer Science10.1007/s42979-021-01010-y3:2Online publication date: 12-Jan-2022
  • (2022)Towards Real-World HDRTV Reconstruction: A Data Synthesis-Based ApproachComputer Vision – ECCV 202210.1007/978-3-031-19800-7_12(199-216)Online publication date: 9-Nov-2022
  • (2021)De-ghosted HDR video acquisition for embedded systemsJournal of Real-Time Image Processing10.1007/s11554-020-01001-x18:3(659-668)Online publication date: 1-Jun-2021
  • (2019)High Dynamic Range Image Deghosting Using Spectral Angle MapperComputers10.3390/computers80100158:1(15)Online publication date: 9-Feb-2019
  • (2016)An objective deghosting quality metric for HDR imagesProceedings of the 37th Annual Conference of the European Association for Computer Graphics10.5555/3058909.3058928(139-152)Online publication date: 9-May-2016
  • (2016)An Objective Deghosting Quality Metric for HDR ImagesComputer Graphics Forum10.1111/cgf.1281835:2(139-152)Online publication date: 27-May-2016
  • (2015)The State of the Art in HDR DeghostingComputer Graphics Forum10.1111/cgf.1259334:2(683-707)Online publication date: 1-May-2015
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