Dense SIFT for ghost-free multi-exposure fusion
•Two dense SIFT based quality measures for multi-exposure fusion are presented.•A new ghost-free multi-exposure fusion method based on dense SIFT is proposed.•Two weight distribution strategies for local contrast extraction are studied. Due to the limited capture range of common imaging sensors, a s...
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| Published in | Journal of visual communication and image representation Vol. 31; pp. 208 - 224 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.08.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1047-3203 1095-9076 |
| DOI | 10.1016/j.jvcir.2015.06.021 |
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| Abstract | •Two dense SIFT based quality measures for multi-exposure fusion are presented.•A new ghost-free multi-exposure fusion method based on dense SIFT is proposed.•Two weight distribution strategies for local contrast extraction are studied.
Due to the limited capture range of common imaging sensors, a scene with high dynamic range usually cannot be well described by a single still image because some regions in it may be under-exposed or over-exposed. In this paper, a new multi-exposure fusion method based on dense scale invariant feature transform (SIFT) is presented. In our algorithm, the dense SIFT descriptor is first employed as the activity level measurement to extract local details from source images, and then adopted to remove ghosting artifacts when the captured scene is dynamic with moving objects. Furthermore, two popular weight distribution strategies for local contrast extraction, namely, “weighted-average” and “winner-take-all” are studied in this paper. The effects of these two strategies on the fusion results are compared and discussed. Experimental results demonstrate the effectiveness of the proposed method in terms of both visual quality and objective evaluation. |
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| AbstractList | •Two dense SIFT based quality measures for multi-exposure fusion are presented.•A new ghost-free multi-exposure fusion method based on dense SIFT is proposed.•Two weight distribution strategies for local contrast extraction are studied.
Due to the limited capture range of common imaging sensors, a scene with high dynamic range usually cannot be well described by a single still image because some regions in it may be under-exposed or over-exposed. In this paper, a new multi-exposure fusion method based on dense scale invariant feature transform (SIFT) is presented. In our algorithm, the dense SIFT descriptor is first employed as the activity level measurement to extract local details from source images, and then adopted to remove ghosting artifacts when the captured scene is dynamic with moving objects. Furthermore, two popular weight distribution strategies for local contrast extraction, namely, “weighted-average” and “winner-take-all” are studied in this paper. The effects of these two strategies on the fusion results are compared and discussed. Experimental results demonstrate the effectiveness of the proposed method in terms of both visual quality and objective evaluation. |
| Author | Wang, Zengfu Liu, Yu |
| Author_xml | – sequence: 1 givenname: Yu surname: Liu fullname: Liu, Yu email: liuyu1@mail.ustc.edu.cn organization: Department of Automation, University of Science and Technology of China, Hefei 230026, China – sequence: 2 givenname: Zengfu surname: Wang fullname: Wang, Zengfu email: zfwang@ustc.edu.cn organization: Department of Automation, University of Science and Technology of China, Hefei 230026, China |
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| Keywords | High dynamic range imaging Tone mapping Multi-exposure fusion Dense SIFT Quality measure Image fusion Image gradient Ghosting artifacts |
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| SubjectTerms | Dense SIFT Ghosting artifacts High dynamic range imaging Image fusion Image gradient Multi-exposure fusion Quality measure Tone mapping |
| Title | Dense SIFT for ghost-free multi-exposure fusion |
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