Abstract
In this work, a novelvariable-p iteratively weighted algorithm is proposed for image reconstruction. The proposed algorithm introduced a dynamically adjusting strategy for the variable p in the nonconvex l p (0<p<1) norm optimization problem, which can be solved via an iteratively reweighted algorithm. Thus, the image reconstruction procedure could start with the l 1 norm problem which is easy to implement, and approach the original l 0 norm problem during the iteration. Numerical experiments indicate that the proposed algorithm can reconstruct the images with fewer measurements than existing fixed-p algorithms.
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Liu, Z., Zhen, X. (2011). Variable-p Iteratively Weighted Algorithm for Image Reconstruction. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_43
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DOI: https://doi.org/10.1007/978-3-642-23887-1_43
Publisher Name: Springer, Berlin, Heidelberg
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