Improved Radiometric Calibration by Brightness Transfer Function Based Noise & Outlier Removal and Weighted Least Square Minimization


IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.8   pp.2101-2114
Publication Date: 2018/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7380
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
radiometric calibration,  camera response function,  brightness transfer function,  noise & outlier rejection,  weighted least square minimization,  

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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.