Face Hallucination via Locality-Constrained Low-Rank Representation
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© 2016 IEEE. Face hallucination (FH) based on sparse representation (SR) and locality-constrained representation (LCR) gives reasonably good performance. However, neither SR-nor LCR-based methods make full use of the structure information in the training data. On the other hand, low-rank representation (LRR) has been utilized to cluster samples into their respective classes by exploiting low-rank structures of the data. In this paper, we propose a locality-constrained low-rank representation (LCLRR) method to take advantage of both LCR and LRR for FH. LCLRR first enforces a low-rank constraint on choosing the dictionary atoms that belong to a subspace that correspond to the same cluster, it then imposes a locality constraint on selecting atoms that are in the vicinity of test samples. Experiments show that LCLRR outperforms both SR- and LCR-based methods on subjectively and objectively, proving that exploiting the structure information in the training data is feasible in face hallucination.
author list (cited authors)
Lu, T., Xiong, Z., Wan, Y., & Yang, W.