Probabilistic Exposure Fusion

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dc.contributor.author Song, Mingli en_US
dc.contributor.author Tao, Dacheng en_US
dc.contributor.author Chen, Chun en_US
dc.contributor.author Bu, Jiajun en_US
dc.contributor.author Luo, Jiebo en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:33:45Z
dc.date.available 2012-10-12T03:33:45Z
dc.date.issued 2012 en_US
dc.identifier 2011001789 en_US
dc.identifier.citation Song Mingli et al. 2012, 'Probabilistic Exposure Fusion', IEEE-Inst Electrical Electronics Engineers Inc, vol. 21, no. 1, pp. 341-357. en_US
dc.identifier.issn 1057-7149 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18258
dc.description.abstract The luminance of a natural scene is often of high dynamic range (HDR). In this paper, we propose a new scheme to handle HDR scenes by integrating locally adaptive scene detail capture and suppressing gradient reversals introduced by the local adaptation. The proposed scheme is novel for capturing an HDR scene by using a standard dynamic range (SDR) device and synthesizing an image suitable for SDR displays. In particular, we use an SDR capture device to record scene details (i.e., the visible contrasts and the scene gradients) in a series of SDR images with different exposure levels. Each SDR image responds to a fraction of the HDR and partially records scene details. With the captured SDR image series, we first calculate the image luminance levels, which maximize the visible contrasts, and then the scene gradients embedded in these images. Next, we synthesize an SDR image by using a probabilistic model that preserves the calculated image luminance levels and suppresses reversals in the image luminance gradients. The synthesized SDR image contains much more scene details than any of the captured SDR image. Moreover, the proposed scheme also functions as the tone mapping of an HDR image to the SDR image, and it is superior to both global and local tone mapping operators. This is because global operators fail to preserve visual details when the contrast ratio of a scene is large, whereas local operators often produce halos in the synthesized SDR image. The proposed scheme does not require any human interaction or parameter tuning for different scenes. Subjective evaluations have shown that it is preferred over a number of existing approaches. en_US
dc.language English en_US
dc.publisher IEEE-Inst Electrical Electronics Engineers Inc en_US
dc.relation.isbasedon en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/TIP.2011.2157514 en_US
dc.title Probabilistic Exposure Fusion en_US
dc.parent IEEE Transactions On Image Processing en_US
dc.journal.volume 21 en_US
dc.journal.number 1 en_US
dc.publocation Piscataway en_US
dc.identifier.startpage 341 en_US
dc.identifier.endpage 357 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000066550 en_US
dc.personcode 111502 en_US
dc.personcode 0000072392 en_US
dc.personcode 0000073871 en_US
dc.personcode 0000066522 en_US
dc.personcode 011221 en_US
dc.percentage 34 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Dynamic range, probabilistic model, scene modeling en_US
dc.staffid 011221 en_US


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