期刊
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
卷 -, 期 -, 页码 4020-4029出版社
IEEE
DOI: 10.1109/ICCV.2017.431
关键词
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资金
- National Natural Science Founding of China [61171142, U1636218]
- Fundamental Research Funds for Central Universities [2017MS045]
- Guangzhou Key Lab of Body Data Science [201605030011]
- Australian Research Council Projects [FL-170100117, DP-140102164, LP-150100671]
We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image. The 2D image formed from 3D object in the scene is affected by the intrinsic properties (shape and texture) and the extrinsic property (illumination). Based on a novel structure-preserving measure called local variation deviation, a joint intrinsic-extrinsic prior model is proposed for better representation. Better than conventional Retinex models, the proposed model can preserve the structure information by shape prior, estimate the reflectance with fine details by texture prior, and capture the luminous source by illumination prior. Experimental results demonstrate the effectiveness of the proposed method on simulated and real data. Compared with the other Retinex algorithms and state-of-the-art algorithms, the proposed model yields better results on both subjective and objective assessments.
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