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.
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