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From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images

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Figure

Figure 1 presents the behavior of the scalar weight function |x| p —the core of the norm computation—for various values of p, showing that as p goes to zero, this measure becomes a count of the nonzeros in x.
Fig. 3 Probability of success of pursuit algorithms in the stable recovery of the sparsest solution of the linear system Ax = b in the presence of noise.
Fig. 4 (a) Phase transition behavior of  1 minimization. Shaded attribute: fraction of cases in which
Figure 5 presents compressed sensing of dynamic MRI—real-time acquisition of heart motion—by Michael Lustig and coworkers at the Stanford MRI lab [112, 111].
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