Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for
-Regularization Using the Multiple Sub-Dictionary Representation
College of Electronic and Optical Engineering & College of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Research Organization of Electrical Communication, Tohoku University, Sendai 980-8577, Japan
Author to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 9 December 2017 / Accepted: 13 December 2017 / Published: 15 December 2017
are two typical non-convex regularizations of
), which can be employed to obtain a sparser solution than the
regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in
regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases
based on an iterative
thresholding algorithm and then proposes a sparse adaptive iterative-weighted
thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based
regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding
algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based
case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Li, Y.; Zhang, J.; Fan, S.; Yang, J.; Xiong, J.; Cheng, X.; Sari, H.; Adachi, F.; Gui, G. Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for
-Regularization Using the Multiple Sub-Dictionary Representation. Sensors 2017, 17, 2920.
Li Y, Zhang J, Fan S, Yang J, Xiong J, Cheng X, Sari H, Adachi F, Gui G. Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for
-Regularization Using the Multiple Sub-Dictionary Representation. Sensors. 2017; 17(12):2920.
Li, Yunyi; Zhang, Jie; Fan, Shangang; Yang, Jie; Xiong, Jian; Cheng, Xiefeng; Sari, Hikmet; Adachi, Fumiyuki; Gui, Guan. 2017. "Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for
-Regularization Using the Multiple Sub-Dictionary Representation." Sensors 17, no. 12: 2920.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.