Keywords: Image Reconstruction, Sparse & Low-Rank Models
Motivation: The reconstruction quality of CS-MRI is significantly affected by the selection of shrinkage threshold.
Goal(s): Find a self-adaptive threshold for every iteration, every slice and every wavelet sub-band in compressed sensing reconstruction.
Approach: We propose an adaptive threshold selection method by combining an bayes-based adaptive wavelet shrinkage denoising method with compressed sensing reconstruction.
Results: Our threshold based on the coefficients in sparse transform domain has a better reconstruction performance compared with an optimal fixed threshold.
Impact: We propose an adaptive threshold selection method for compressed sensing reconstruction, which promote the reconstruction quality and avoid the manual selection of parameter.
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