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Modal Regression-Based Graph Representation for Noise Robust Face Hallucination
Liu, Licheng1; Chen, C. L.P.2; Wang, Yaonan3
2021
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
AbstractManifold learning-based face hallucination technologies have been widely developed during the past decades. However, the conventional learning methods always become ineffective in noise environment due to the least-square regression, which usually generates distorted representations for noisy inputs they employed for error modeling. To solve this problem, in this article, we propose a modal regression-based graph representation (MRGR) model for noisy face hallucination. In MRGR, the modal regression-based function is incorporated into graph learning framework to improve the resolution of noisy face images. Specifically, the modal regression-induced metric is used instead of the least-square metric to regularize the encoding errors, which admits the MRGR to robust against noise with uncertain distribution. Moreover, a graph representation is learned from feature space to exploit the inherent typological structure of patch manifold for data representation, resulting in more accurate reconstruction coefficients. Besides, for noisy color face hallucination, the MRGR is extended into quaternion (MRGR-Q) space, where the abundant correlations among different color channels can be well preserved. Experimental results on both the grayscale and color face images demonstrate the superiority of MRGR and MRGR-Q compared with several state-of-the-art methods.
KeywordFace recognition Faces Graph learning Image color analysis Image reconstruction modal regression Noise measurement quaternion representation robust face hallucination Superresolution Training
DOI10.1109/TNNLS.2021.3106773
URLView the original
Language英語English
Scopus ID2-s2.0-85114752965
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Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: lichenghnu@gmail.com)
2.School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China, and also with the Faculty of Science and Technology, University of Macau, Macau 999078, China.
3.College of Electrical and Information Engineering, Hunan University, Changsha 410082, China.
Recommended Citation
GB/T 7714
Liu, Licheng,Chen, C. L.P.,Wang, Yaonan. Modal Regression-Based Graph Representation for Noise Robust Face Hallucination[J]. IEEE Transactions on Neural Networks and Learning Systems,2021.
APA Liu, Licheng,Chen, C. L.P.,&Wang, Yaonan.(2021).Modal Regression-Based Graph Representation for Noise Robust Face Hallucination.IEEE Transactions on Neural Networks and Learning Systems.
MLA Liu, Licheng,et al."Modal Regression-Based Graph Representation for Noise Robust Face Hallucination".IEEE Transactions on Neural Networks and Learning Systems (2021).
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