RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING

High dynamic range imaging aims at creating an image with a range of intensity variations larger than the range supported by a camera sensor. Most commonly used methods combine multiple exposure low dynamic range (LDR) images, to obtain the high dynamic range (HDR) image. Available methods typically...

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Main Author: M.R. Renu
Format: Article
Language:English
Published: ICT Academy of Tamil Nadu 2013-02-01
Series:ICTACT Journal on Image and Video Processing
Subjects:
Online Access:http://ictactjournals.in/paper/IJIVP(2013)_Vol3_Iss3_Paper2_551_558.pdf
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spelling doaj-a701e9ce12284b24885d80ca8ada9d272020-11-24T21:25:45ZengICT Academy of Tamil NaduICTACT Journal on Image and Video Processing0976-90990976-91022013-02-0133551558 RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGINGM.R. Renu0Vision and Image Processing Laboratory, Department of Electrical Engineering, Indian Institute of Technology, Bombay, IndiaHigh dynamic range imaging aims at creating an image with a range of intensity variations larger than the range supported by a camera sensor. Most commonly used methods combine multiple exposure low dynamic range (LDR) images, to obtain the high dynamic range (HDR) image. Available methods typically neglect the noise term while finding appropriate weighting functions to estimate the camera response function as well as the radiance map. We look at the HDR imaging problem in a denoising frame work and aim at reconstructing a low noise radiance map from noisy low dynamic range images, which is tone mapped to get the LDR equivalent of the HDR image. We propose a maximum aposteriori probability (MAP) based reconstruction of the HDR image using Gibb’s prior to model the radiance map, with total variation (TV) as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided.http://ictactjournals.in/paper/IJIVP(2013)_Vol3_Iss3_Paper2_551_558.pdfHigh Dynamic Range ImagingDenoisingMaximum Aposteriori Probability (MAP)Total Variation (TV)Majorize-Minimize
collection DOAJ
language English
format Article
sources DOAJ
author M.R. Renu
spellingShingle M.R. Renu
RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING
ICTACT Journal on Image and Video Processing
High Dynamic Range Imaging
Denoising
Maximum Aposteriori Probability (MAP)
Total Variation (TV)
Majorize-Minimize
author_facet M.R. Renu
author_sort M.R. Renu
title RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING
title_short RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING
title_full RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING
title_fullStr RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING
title_full_unstemmed RADIANCE DOMAIN COMPOSITING FOR HIGH DYNAMIC RANGE IMAGING
title_sort radiance domain compositing for high dynamic range imaging
publisher ICT Academy of Tamil Nadu
series ICTACT Journal on Image and Video Processing
issn 0976-9099
0976-9102
publishDate 2013-02-01
description High dynamic range imaging aims at creating an image with a range of intensity variations larger than the range supported by a camera sensor. Most commonly used methods combine multiple exposure low dynamic range (LDR) images, to obtain the high dynamic range (HDR) image. Available methods typically neglect the noise term while finding appropriate weighting functions to estimate the camera response function as well as the radiance map. We look at the HDR imaging problem in a denoising frame work and aim at reconstructing a low noise radiance map from noisy low dynamic range images, which is tone mapped to get the LDR equivalent of the HDR image. We propose a maximum aposteriori probability (MAP) based reconstruction of the HDR image using Gibb’s prior to model the radiance map, with total variation (TV) as the prior to avoid unnecessary smoothing of the radiance field. To make the computation with TV prior efficient, we extend the majorize-minimize method of upper bounding the total variation by a quadratic function to our case which has a nonlinear term arising from the camera response function. A theoretical justification for doing radiance domain denoising as opposed to image domain denoising is also provided.
topic High Dynamic Range Imaging
Denoising
Maximum Aposteriori Probability (MAP)
Total Variation (TV)
Majorize-Minimize
url http://ictactjournals.in/paper/IJIVP(2013)_Vol3_Iss3_Paper2_551_558.pdf
work_keys_str_mv AT mrrenu radiancedomaincompositingforhighdynamicrangeimaging
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