Abstract
The camera response function (CRF) that maps linear irradiance to pixel intensities must be known for computational imaging applications that match features in images with different exposures. This function is scene dependent and is difficult to estimate in scenes with significant motion. In this paper, we present a novel algorithm for radiometric calibration from multiple exposure images of a dynamic scene. Our approach is based on two key ideas from the literature: (1) intensity mapping functions which map pixel values in one image to the other without the need for pixel correspondences, and (2) a rank minimization algorithm for radiometric calibration. Although each method has its problems, we show how to combine them in a formulation that leverages their benefits. Our algorithm recovers the CRFs for dynamic scenes better than previous methods, and we show how it can be applied to existing algorithms such as those for high-dynamic range imaging to improve their results.
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Bibtex
@inproceedings{Badki2015, author = {Abhishek Badki and Nima Khademi Kalantari and Pradeep Sen},
title = {{Robust Radiometric Calibration for Dynamic Scenes in the Wild}},
booktitle = {Proceedings of the International Conference on Computational Photography (ICCP)},
month = apr,
year = {2015}, }
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