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Then, the images can be reconstructed by solving Eq. (18) for each target separately.

Given the measurements, the signal can be reconstructed by solving the following problem: (3).

When the above condition holds, the expansion coefficients θ can be reconstructed by solving the following l0-norm constrained optimization problem: θ ⌢ = arg min θ 0 s.

The expression Y = ΦXshows that the original signal Y can be sparsely represented in Φ. Y can be reconstructed by solving the L0-minimization problem.

The cross-range image β m of mth range cell can be reconstructed by solving min {leftVert {widehat{beta}}_mrightVert}_1kern1.25em mathrm{leftVertm{y}.kern0.75em {leftVert {y}_m-varPhi varPsi beta rightVert}_2le varepsilon (18 where ( {widehat{beta}}_m ) is the reconstruction of vector β and ε is the noise level.

Candes et al. have proved that if the number of measurement M=O(K log(N)), and the measurement matrix satisfies the constraint of restricted isometry property (RIP), signal x can be reconstructed by solving the l0-norm minimization in (2) [3].

In classical approach, the phenomenological model is used to simulate the number of counts measured by the detector and then the tracer position is reconstructed by solving a minimisation problem between the measured events and the mentioned model.

In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients.

The fluorescent image was reconstructed by solving the inverse problem using our previously developed regularized algorithm [ 21].

To solve this problem, we classify Ly based on how well it can be reconstructed by using the coefficients from each class as in [ 17].

Such defects should immediately be reconstructed by classic grafts.