Abstract
Face hallucination is a special super-resolution (SR) algorithm that enhances the resolution and quality of low-resolution (LR) facial image. For reconstructing finer high frequency information which are missing in image degradation, learning-based face SR methods rely on accurate prior information from training samples. In this paper, we propose a contextual-field supported iterative representation algorithm for face hallucination to discovery accurate prior. Different from traditional local-patch based methods, we use contextual-field supported sampling to replace local receptive field patch sampling for enriching prior information. Then, two weighted matrices are introduced to constrain reconstruction-errors term and representation-coefficients term simultaneously, one matrix ameliorates the heteroscedasticity of real data and the other one improves the stability of solution. Finally, we use iterative representation learning to iteratively update the supported dictionary pairs and their representation-coefficients to refine accurate high-frequency information. The experimental results show that the proposed approach outperforms some state-of-the-art face hallucination methods over FERET and CMU-MIT face databases using both subjective and objective evaluation indexes.
This work is supported by the National Natural Science Foundation of China (61502354, 61501413, 61671332, 41501505, U1404618), the Natural Science Foundation of Hubei Province of China (2018ZYYD059, 2015CFB451, 2014CFA130, 2012FFA099, 2012FFA134, 2013CF125), the Science and Technique Development Program of He’nan (172102210186), Scientific Research Foundation of Wuhan Institute of Technology (K201713), Graduate Education Innovation Foundation of Wuhan Institute of Technology (CX2017069, CX2017070).
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Zeng, K., Lu, T., Li, X., Zhang, Y., Peng, L., Qu, S. (2018). Contextual-Field Supported Iterative Representation for Face Hallucination. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_40
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