Sparse representation-based classification (SRC) and collaborative representation-based classification (CRC) have garnered significant attention recently. In CRC, it is argued that it is the collaborative representation mechanism but not the ℓ₁-norm sparsity that makes SRC successful for classification tasks. However, recent studies reveal that sparsity does play a critical role in accurate classification, thus it should not be totally overlooked due to relatively high computational cost. Inspired by these findings, we propose a method called sparsity augmented weighted collaborative representation-based classification (SA-WCRC) for image classification. First, the representation coefficients of the test sample are obtained via weighted collaborative representation and sparse representation, respectively. Second, we augment the coefficient obtained by weighted collaborative representation with the sparse representation. Finally, the test sample is classified based on the augmented coefficient and the label matrix of the training samples. Both the augmented coefficient and classification scheme make SA-WCRC efficient for classification. Experiments on three face databases and one scene dataset demonstrate the superiority of SA-WCRC over its counterparts.