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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2020 - 1
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De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue

SHIM, S.-O See more information about SHIM, S.-O on SCOPUS See more information about SHIM, S.-O on IEEExplore See more information about SHIM, S.-O on Web of Science, ALHARBI, S. See more information about  ALHARBI, S. on SCOPUS See more information about  ALHARBI, S. on SCOPUS See more information about ALHARBI, S. on Web of Science, KHAN, I. R. See more information about  KHAN, I. R. on SCOPUS See more information about  KHAN, I. R. on SCOPUS See more information about KHAN, I. R. on Web of Science, AZIZ, W. See more information about AZIZ, W. on SCOPUS See more information about AZIZ, W. on SCOPUS See more information about AZIZ, W. on Web of Science
 
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Download PDF pdficon (1,293 KB) | Citation | Downloads: 440 | Views: 598

Author keywords
image sequence analysis, image fusion, image reconstruction, image motion analysis, image quality

References keywords
dynamic(26), range(20), high(18), image(17), images(15), comput(13), imaging(12), tone(10), process(10), pattern(9)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-08-31
Volume 20, Issue 3, Year 2020, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.03001
Web of Science Accession Number: 000564453800001
SCOPUS ID: 85090343221

Abstract
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A High Dynamic Range (HDR) image produced from a sequence of low dynamic range (LDR) images can contain motion artefacts (ghosting) if the scene contains moving objects. Conventional de-ghosting methods first detect moving objects in the scene, and then either remove those moving objects totally or reconstruct them. However, these methods are computationally expensive. This paper proposes a de-ghosting method that does not require explicit detection of moving regions. First, the ratio between camera exposure times of a target image and a reference image, which is called the intensity scaling factor in this paper, is computed. Since the information about camera exposure time is not available always, we propose a novel method to estimate the intensity scaling factor from non-saturated and non-moving pixels. Then, the estimated scaling factor is used as a cue to label every pixel in the target image as either static or moving pixel. Finally, the values of moving pixels are corrected with their expected values which can be estimated from the intensity scaling factor. Experimental results show that the proposed method generates more accurate ghost-free HDR images than the existing state of the art methods.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 1,852 TCR
SCOPUS® Citations for all references: 3,204 TCR

Web of Science® Average Citations per reference: 37 ACR
SCOPUS® Average Citations per reference: 64 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-03-06 09:31 in 288 seconds.




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