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Cited by 1,081

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation

Fu, Xueyang, Zeng, Delu, Huang, Yue, Zhang, Xiao-Ping, Ding, Xinghao·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2016.304
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Abstract

We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image. We show that, though it is widely adopted for ease of modeling, the log-transformed image for this task is not ideal. Based on the previous investigation of the logarithmic transformation, a new weighted variational model is proposed for better prior representation, which is imposed in the regularization terms. Different from conventional variational models, the proposed model can preserve the estimated reflectance with more details. Moreover, the proposed model can suppress noise to some extent. An alternating minimization scheme is adopted to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed model with its algorithm. Compared with other variational methods, the proposed method yields comparable or better results on both subjective and objective assessments.

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Metadata

Authors
Fu, X., Zeng, D., Huang, Y., Zhang, X.-P., & Ding, X.
Venue
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2016-06
Volume
2016
Pages
2782-2790
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2016.304
OpenAlex
W2468596194
Cited by
1,081
References
24
FWCI
13.51
Type
conference paper
Language
en
Open access
closed

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