Locality-Constrained Double Low-Rank Representation for Effective Face Hallucination

Abstract

Recently, position-patch-based face hallucination methods have received much attention, and obtained promising progresses due to their effectiveness and efficiency. A locality-constrained double low-rank representation (LCDLRR) method is proposed for effective face hallucination in this paper. LCDLRR attempts to directly use the image-matrix based regression model to compute the representation coefficients to maintain the essential structural information. On the other hand, LCDLRR imposes a low-rank constraint on the representation coefficients to adaptively select the training samples that belong to the same subspace as the inputs. Moreover, a locality constraint is also enforced to preserve the locality and the sparsity simultaneously. Compared with previous methods, our proposed LCDLRR considers locality manifold structure, cluster constraints, and structure error simultaneously. Extensive experimental results on standard face hallucination databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

Keywords

• Face hallucination
• low-rank representation
• position-patch
• nuclear norm