A sparse regularization algorithm with an adaptive shrinkage thresholding operator is proposed. In the operator the thresholding parameter can be updated according to the sparsity of the solution during iteration process, since the thresholding parameter is hard to select in sparse regularization algorithm for image reconstruction. In addition, a weight coefficient is introduced in the thresholding operator, and the effect of attenuation characteristics on image reconstruction quality is studied. The feasibility of this proposed algorithm is validated by image reconstruction of electrical tomography. The results show that the performance of images reconstructed using the sparse regularization algorithm with the proposed adaptive shrinkage thresholding operator is better than that using traditional shrinkage thresholding algorithm. When the distribution of the measured field is relatively simple, a larger weight coefficient is adopted. When the distribution of the measured field is relatively complicated, a smaller weight coefficient is used, which is beneficial to improve the quality of the reconstructed image.
ZHANG Shengnan
,
XU Yanbin
,
DONG Feng
. Sparse regularization algorithm with adaptive shrinkage thresholding operator for image reconstruction[J]. Journal of University of Chinese Academy of Sciences, 2020
, 37(2)
: 242
-247
.
DOI: 10.7523/j.issn.2095-6134.2020.02.014
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