IEICE TRANSACTIONS on Information
Improved Radiometric Calibration by Brightness Transfer Function Based Noise & Outlier Removal and Weighted Least Square Minimization
Summary :
An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.8 pp.2101-2114
- Publication Date
- 2018/08/01
- Publicized
- 2018/05/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7380
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Chanchai TECHAWATCHARAPAIKUL
King Mongkut University of Technology Thounburi (KMUTT)
Pradit MITTRAPIYANURUK
Srinakharinwirot University
Pakorn KAEWTRAKULPONG
King Mongkut University of Technology Thounburi (KMUTT)
Supakorn SIDDHICHAI
Digital Economy Promotion Agency (DEPA)
Werapon CHIRACHARIT
King Mongkut University of Technology Thounburi (KMUTT)
Keyword
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Chanchai TECHAWATCHARAPAIKUL, Pradit MITTRAPIYANURUK, Pakorn KAEWTRAKULPONG, Supakorn SIDDHICHAI, Werapon CHIRACHARIT, "Improved Radiometric Calibration by Brightness Transfer Function Based Noise & Outlier Removal and Weighted Least Square Minimization" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 8, pp. 2101-2114, August 2018, doi: 10.1587/transinf.2017EDP7380.
Abstract: An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7380/_p
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@ARTICLE{e101-d_8_2101,
author={Chanchai TECHAWATCHARAPAIKUL, Pradit MITTRAPIYANURUK, Pakorn KAEWTRAKULPONG, Supakorn SIDDHICHAI, Werapon CHIRACHARIT, },
journal={IEICE TRANSACTIONS on Information},
title={Improved Radiometric Calibration by Brightness Transfer Function Based Noise & Outlier Removal and Weighted Least Square Minimization},
year={2018},
volume={E101-D},
number={8},
pages={2101-2114},
abstract={An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.},
keywords={},
doi={10.1587/transinf.2017EDP7380},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Improved Radiometric Calibration by Brightness Transfer Function Based Noise & Outlier Removal and Weighted Least Square Minimization
T2 - IEICE TRANSACTIONS on Information
SP - 2101
EP - 2114
AU - Chanchai TECHAWATCHARAPAIKUL
AU - Pradit MITTRAPIYANURUK
AU - Pakorn KAEWTRAKULPONG
AU - Supakorn SIDDHICHAI
AU - Werapon CHIRACHARIT
PY - 2018
DO - 10.1587/transinf.2017EDP7380
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2018
AB - An improved radiometric calibration algorithm by extending the Mitsunaga and Nayar least-square minimization based algorithm with two major ideas is presented. First, a noise & outlier removal procedure based on the analysis of brightness transfer function is included for improving the algorithm's capability on handling noise and outlier in least-square estimation. Second, an alternative minimization formulation based on weighted least square is proposed to improve the weakness of least square minimization when dealing with biased distribution observations. The performance of the proposed algorithm with regards to two baseline algorithms is demonstrated, i.e. the classical least square based algorithm proposed by Mitsunaga and Nayar and the state-of-the-art rank minimization based algorithm proposed by Lee et al. From the results, the proposed algorithm outperforms both baseline algorithms on both the synthetic dataset and the dataset of real-world images.
ER -
English
