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          Institute: MPI für Informatik     Collection: Computer Graphics Group     Display Documents



ID: 537323.0, MPI für Informatik / Computer Graphics Group
Optimal HDR reconstruction with linear digital cameras
Authors:Granados, Miguel; Adjin, Boris; Wand, Michael; Theobalt, Christian; Seidel, Hans-Peter; Lensch, Hendrik P. A.
Language:English
Publisher:IEEE
Place of Publication:Piscataway, NJ
Date of Publication (YYYY-MM-DD):2010
Title of Proceedings:2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010). - Pt. 1
Start Page:215
End Page:222
Place of Conference/Meeting:San Francisco, USA
(Start) Date of Conference/Meeting
 (YYYY-MM-DD):
2010-06-13
End Date of Conference/Meeting 
 (YYYY-MM-DD):
2010-06-18
Copyright:This CD-ROM product was produced for the 2010 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR) & 2010 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) by
Omnipress. Duplication of this CD-ROM and its content in print or digital form
for the purpose of sharing with others is prohibited without permission from
2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) &
2010 IEEE Computer Society Conference on Computer Vision and Pattern
Recognition Workshops (CVPRW) and Omnipress. Also, copying this product's
instructions and/or designs for use on future CD-ROMs or digital products is
prohibited without written permission from Omnipress.
Audience:Experts Only
Intended Educational Use:No
Abstract / Description:Given a multi-exposure sequence of a scene, our aim is to recover the absolute
irradiance falling onto a linear camera sensor. The established approach is to
perform a weighted average of the scaled input exposures. However, there is no
clear consensus on the appropriate weighting to use. We propose a weighting
function that produces statistically optimal estimates under the assumption of
compound- Gaussian noise. Our weighting is based on a calibrated camera model
that accounts for all noise sources. This model also allows us to
simultaneously estimate the irradiance and its uncertainty. We evaluate our
method on simulated and real world photographs, and show that we consistently
improve the signal-to-noise ratio over previous approaches. Finally, we show
the effectiveness of our model for optimal exposure sequence selection and HDR
image denoising.
Last Change of the Resource (YYYY-MM-DD):2011-01-21
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Hans-Peter Seidel
Affiliations:MPI für Informatik/Computer Graphics Group
Identifiers:LOCALID:C125675300671F7B-806F7C9277D20004C125781D0034E204-...
URL:http://www.mpi-inf.mpg.de/~granados/papers/granado...
DOI:10.1109/CVPR.2010.5540208
ISBN:978-1-4244-6983-3
Full Text:
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