In recent years, high dimensional data reconstruction based on sparse tensor has been one promising research field, especially for color images reconstruction and denoising magnetic resonance images. This paper aims at developing a new high dimensional data reconstruction model based on L-2,L-1-norm. A fast algorithm is devised to solve the optimal problem. Additionally, error bound analysis of proposed scheme is discussed. Experiments show that the proposed algorithm is efficient, and the reconstruction results are better than two state-of-the-art methods. The proposed method has higher efficiency in terms of computational time and iterations. (C) 2020 Elsevier Inc. All rights reserved.
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