Modeling the space of camera response functions

Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image intensity of an imaging system is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response func...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2004-10, Vol.26 (10), p.1272-1282
Main Authors: Grossberg, M.D., Nayar, S.K.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
calibration
camera response function
Cameras
Cluster Analysis
Computer Graphics
Computer Simulation
Constraint theory
Digital cameras
empirical modeling
Equipment Failure Analysis - methods
gamma correction
Geometry
high-dynamic range
Image databases
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Index Terms- Radiometric response function
Layout
Models, Theoretical
nonlinear response
Numerical Analysis, Computer-Assisted
Optical films
Optical imaging
Pattern Recognition, Automated - methods
Photography - instrumentation
Photography - methods
Photometry
real-world response curves
recovery of radiometry
Reflectivity
Sensitivity and Specificity
sensor modeling
Shape measurement
Signal Processing, Computer-Assisted
Studies
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Summary:Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image intensity of an imaging system is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database, we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter empirical model of response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at http://www.cs.columbia.edu/CAVE.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2004.88