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The Weighted L 2,1 Minimization for Partially Known Support

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Abstract

A weighted L 2,1 minimization is proposed for signal reconstruction from a limited number of measurements when partial support information is known. The reconstruction error bound of the weighted L 2,1 minimization is obtained and our sufficient condition is shown to be better than \(\delta _{3K}<{\frac{1}{\sqrt{3}}}\) if the estimated support is at least 50 % accurate. Experiments are given for larynx image sequence to illustrate the validity of the proposed method.

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Notes

  1. The codes can be downloaded from the first author’s homepage.

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Acknowledgments

This work was supported by the Scientific Research Foundation for Ph.D. of Henan Normal University (No. qd14142), the Key Scientific Research Project of Colleges and Universities in Henan Province (No. 15B120004) and National Natural Science Foundation of China, Tian Yuan Special Foundation (No. 11526081).

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  1. Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China

    Haifeng Li

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  1. Haifeng Li
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Correspondence to Haifeng Li.

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Li, H. The Weighted L 2,1 Minimization for Partially Known Support. Wireless Pers Commun 91, 255–265 (2016). https://doi.org/10.1007/s11277-016-3458-7

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