ML Anthology

Radiometric Calibration by Transform Invariant Low-Rank Structure

Joon-Young Lee, Boxin Shi, Yasuyuki Matsushita, In-So Kweon, Katsushi Ikeuchi
CVPR 2011 pp. 2337-2344
doi:10.1109/CVPR.2011.5995409 /cvpr/2011/lee2011cvpr-radiometric/

Abstract

We present a robust radiometric calibration method that capitalizes on the transform invariant low-rank structure of sensor irradiances recorded from a static scene with different exposure times. We formulate the radiometric calibration problem as a rank minimization problem. Unlike previous approaches, our method naturally avoids over-fitting problem; therefore, it is robust against biased distribution of the input data, which is common in practice. When the exposure times are completely unknown, the proposed method can robustly estimate the response function up to an exponential ambiguity. The method is evaluated using both simulation and real-world datasets and shows a superior performance than previous approaches.

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Cite

Text

Lee et al. "Radiometric Calibration by Transform Invariant Low-Rank Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995409

Markdown

[Lee et al. "Radiometric Calibration by Transform Invariant Low-Rank Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/lee2011cvpr-radiometric/) doi:10.1109/CVPR.2011.5995409

BibTeX

@inproceedings{lee2011cvpr-radiometric,
  title     = {{Radiometric Calibration by Transform Invariant Low-Rank Structure}},
  author    = {Lee, Joon-Young and Shi, Boxin and Matsushita, Yasuyuki and Kweon, In-So and Ikeuchi, Katsushi},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {2337-2344},
  doi       = {10.1109/CVPR.2011.5995409},
  url       = {https://mlanthology.org/cvpr/2011/lee2011cvpr-radiometric/}
}