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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References
Candès, E.: Compressive sampling. In: International Congress of Mathematics, Madrid, Spain, pp. 1433–1452 (2006)
Candès, E., Wakin, M.: An Introduction to Compressive Sampling. IEEE Signal Processing Magazine 25(2), 21–30 (2008)
Chen, S., Donoho, D., Saunders, M.: Atomic decomposition by basis pursuit. SIAM Review 43(1), 129–159 (2001)
Yang, Y., Liu, Z., Lv, F.Y.: Signal recovery based on iterative weighted l1 norm. Computer Engineering and Applications 46(3), 128–130 (2010)
Chartrand, R.: Exact reconstructions of sparse signals via nonconvex minimization. IEEE Signal Proc. Lett. 14(10), 707–710 (2007)
Chartrand, R., Yin, W.: Iteratively Reweighted Algorithms For Compressive Sensing. In: 33rd International Conference on Acoustics, Speech, and Signal Processing (2008)
Chartrand, R., Staneva, V.: Restricted isometry properties and nonconvex compressive sensing. Inverse Problems 24 (2008)
Domíngueza, R.J., Cabrerab, S.D., Rosilesb, J.G., Pineda, J.V.: Reconstruction in compressive sensing using affine scaling transformations with variable-p diversity measure. In: Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop (2009)
Candès, E., Wakin, M., Boyd, S.: Enhancing Sparsity by Reweighted l1 Minimization. Joural of Fourier Analysis and Applications 14, 877–905 (2008)
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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
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
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