De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue
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 o...
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Stefan cel Mare University of Suceava
2020-08-01
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Series: | Advances in Electrical and Computer Engineering |
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Online Access: | http://dx.doi.org/10.4316/AECE.2020.03001 |
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doaj-2d5f402fa9d8413fb3f9389ab5f4ba8d2020-11-25T03:41:58ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002020-08-0120331010.4316/AECE.2020.03001De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling CueSHIM, S.-OALHARBI, S.KHAN, I. R.AZIZ, W.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.http://dx.doi.org/10.4316/AECE.2020.03001image sequence analysisimage fusionimage reconstructionimage motion analysisimage quality |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
SHIM, S.-O ALHARBI, S. KHAN, I. R. AZIZ, W. |
spellingShingle |
SHIM, S.-O ALHARBI, S. KHAN, I. R. AZIZ, W. De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue Advances in Electrical and Computer Engineering image sequence analysis image fusion image reconstruction image motion analysis image quality |
author_facet |
SHIM, S.-O ALHARBI, S. KHAN, I. R. AZIZ, W. |
author_sort |
SHIM, S.-O |
title |
De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue |
title_short |
De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue |
title_full |
De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue |
title_fullStr |
De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue |
title_full_unstemmed |
De-ghosting in High Dynamic Range Imaging Based on Intensity Scaling Cue |
title_sort |
de-ghosting in high dynamic range imaging based on intensity scaling cue |
publisher |
Stefan cel Mare University of Suceava |
series |
Advances in Electrical and Computer Engineering |
issn |
1582-7445 1844-7600 |
publishDate |
2020-08-01 |
description |
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. |
topic |
image sequence analysis image fusion image reconstruction image motion analysis image quality |
url |
http://dx.doi.org/10.4316/AECE.2020.03001 |
work_keys_str_mv |
AT shimso deghostinginhighdynamicrangeimagingbasedonintensityscalingcue AT alharbis deghostinginhighdynamicrangeimagingbasedonintensityscalingcue AT khanir deghostinginhighdynamicrangeimagingbasedonintensityscalingcue AT azizw deghostinginhighdynamicrangeimagingbasedonintensityscalingcue |
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