Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0<p<1), which can be employed to obtain a sparser solution than the L₁ regularization.  To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p∈{1/2, 2/3} based on an iterative Lp thresholding algorithm and then proposes a sparse adaptive iterative-weighted Lp thresholding algorithm (SAITA).  Simulation results have shown that the proposed SAITA not only performs better than the corresponding L₁ algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based Lp case. 
SAITA
Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0<p<1), which can be employed to obtain a sparser solution than the L₁ regularization. 
L1/2
Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0<p<1), which can be employed to obtain a sparser solution than the L₁ regularization. 
L2/3
Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0<p<1), which can be employed to obtain a sparser solution than the L₁ regularization. 
Lp-Regularization