Probabilistic exposure fusion.

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Show simple item record Song, M Tao, D Chen, C Bu, J Luo, J Zhang, C 2012-10-12T03:33:45Z 2012-01
dc.identifier.citation IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2012, 21 (1), pp. 341 - 357
dc.identifier.issn 1057-7149
dc.identifier.other C1 en_US
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.
dc.format Print-Electronic
dc.language eng
dc.relation.isbasedon 10.1109/tip.2011.2157514
dc.title Probabilistic exposure fusion.
dc.type Journal Article
dc.description.version Published
dc.parent IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
dc.journal.volume 1
dc.journal.volume 21
dc.journal.number 1 en_US
dc.publocation Piscataway en_US
dc.identifier.startpage 341 en_US
dc.identifier.endpage 357 en_US FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 1702 Cognitive Sciences
dc.for 0801 Artificial Intelligence and Image Processing
dc.for 0906 Electrical and Electronic Engineering
dc.personcode 011221
dc.personcode 111502
dc.percentage 34 en_US Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords Dynamic range, probabilistic model, scene modeling en_US
dc.description.keywords Science & Technology
dc.description.keywords Life Sciences & Biomedicine
dc.description.keywords Nursing
dc.description.keywords NURSING, SCI
dc.description.keywords NURSING, SSCI
dc.description.keywords Near-miss
dc.description.keywords Qualitative study
dc.description.keywords Hysterectomy
dc.description.keywords Postpartum haemorrhage
dc.description.keywords CESAREAN-SECTION
dc.description.keywords TRAUMATIC BIRTH
dc.description.keywords SENSITIVE TOPICS
dc.description.keywords MATERNAL DEATH
dc.description.keywords CHILDBIRTH
dc.description.keywords PREVALENCE
dc.description.keywords DELIVERY
dc.description.keywords HEALTH
dc.description.keywords MISSES
dc.description.keywords Image Interpretation, Computer-Assisted
dc.description.keywords Image Enhancement
dc.description.keywords Data Interpretation, Statistical
dc.description.keywords Sensitivity and Specificity
dc.description.keywords Reproducibility of Results
dc.description.keywords Data Display
dc.description.keywords Lighting
dc.description.keywords Algorithms
dc.description.keywords Pattern Recognition, Automated
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Uncategorised (ID: 363)
utslib.collection.history Closed (ID: 3)

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