Abstract
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 L2,1-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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.