Abstract
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.
Published Version
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