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In this paper, we propose a novel algorithm for single image super-resolution by developing a concept of cluster rather than using patch as the basic unit. For the proposed algorithm, all patches are splitted into numerous subspaces, and the optimal representation problem is solved with jointly low-rank and sparse regularization for each subspace. By enforcing global consistency constraint of each subspace with nuclear norm regularization and capturing local linear structure of each patch with ℓ 1 -norm regularization, effective matching functions for test and exemplar patches can be created. Accordingly, the desirable results with low computational complexity are obtained. Experimental results show that the proposed algorithm generates high-quality images in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: Sparse representation is being proved to be effective for many tasks in the field of face recognition. In this paper, we will propose an efficient face recognition algorithm via sparse representation in 2D Fisherface space. We firstly transformed the 2D image into 2D Fisherface in preprocessing, and classify the testing image via sparse representation in the 2D Fisherface space. Then we extend the proposed method using some supplementary matrices to deal with random pixels corruption. For face image with contiguous occlusion, we partition each image into some blocks, and define a new rule combining sparsity and reconstruction residual to discard the occluded blocks, the final result is aggregated by voting the classification result of the valid individual block. The experimental results have shown that the proposed algorithm achieves a satisfying performance in both accuracy and robustness. [Copyright &y& Elsevier]

Abstract: Sparse representation is being proved to be effective for many tasks in the field of pattern recognition. In this paper, an efficient classification algorithm based on concentrative sparse representation will be proposed to address the problem caused by insufficient training samples in each class. We firstly compute representation coefficient of the testing sample with training samples matrix using subspace pursuit recovery algorithm. Then we define concentration measurement function in order to determine whether the sparse representation coefficient is concentrative. Subspace pursuit is repeatedly used to revise the sparse representation until concentration is met. Such a concentrative sparse representation can contribute to discriminative residuals that are critical to accurate classification. The experimental results have showed that the proposed algorithm achieves a satisfying performance in both accuracy and efficiency. [Copyright &y& Elsevier]