Radiometric Calibration from Noise Distributions
- 1 June 2007
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
A method is proposed for estimating radiometric response functions from noise observations. From the statistical properties of noise sources, the noise distribution for each scene radiance value is shown to be symmetric for a radiometrically calibrated camera. However, due to the non-linearity of camera response functions, the observed noise distributions become skewed in an uncalibrated camera. In this paper, we capitalize on these asymmetric profiles of measured noise distributions to estimate radiometric response functions. Unlike prior approaches, the proposed method is not sensitive to noise level, and is therefore particularly useful when the noise level is high. Also, the proposed method does not require registered input images taken with different exposures; only statistical noise distributions at multiple intensity levels are used. Real-world experiments demonstrate the effectiveness of the proposed approach in comparison to standard calibration techniques.Keywords
This publication has 14 references indexed in Scilit:
- Determining the Radiometric Response Function from a Single Grayscale ImagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Global Likelihood Optimization Via the Cross-Entropy Method, with an Application to Mixture ModelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Radiometric calibration from a single imagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Probability models for high dynamic range imagingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Radiometric self calibrationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Statistical calibration of CCD imaging processPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- High dynamic range imaging: spatially varying pixel exposuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- The noise is the signalNature, 1998
- A Simplex Method for Function MinimizationThe Computer Journal, 1965
- Mathematical Analysis of Random NoiseBell System Technical Journal, 1944