Face Hallucination Via Weighted Adaptive Sparse Regularization

摘要: Sparse representation-based face hallucination approaches proposed so far use fixed ℓ 1 norm penalty to capture the sparse nature of face images, and thus hardly adapt readily to the statistical variability of underlying images. Additionally, they ignore the influence of spatial distances between the test image and training basis images on optimal reconstruction coefficients. Consequently, they cannot offer a satisfactory performance in practical face hallucination applications. In this paper, we propose a weighted adaptive …