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As an extension of the conventional spatial total variation, a new online model called dynamic total variation is used to exploit the sparsity on both spatial and temporal domains in dMRI.
L1 penalty imposes sparsity on both u and v and assumes that each canonical correlation involves only a few features from X and Y.
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Q enforces sparsity on the overall structure of the data and both P and Q are subject to W=P+Q.
To tackle this problem we assume local sparsity on image patches.
We use similar approach of ST in this work to solve for sparsity on ({mathbf{S }}).
Therefore, this sparsity on the phase errors is incorporated into the problem by using the regularization term ∥β−1∥1.
Those methods impose additional constraints, such as localization or sparsity, on the NMF cost function to construct improved NMF decompositions.
They enforce sparsity on the spatial spectrum and pose source localization as an over-complete basis representation problem.
First, the BeBP induces sparsity on the weights w i, and second, it allows information to be shared across the weights during inference.
In order to break the constraint of signal sparsity on MP algorithms, Dohono et al. [18] proposed a stagewise orthogonal matching pursuit (StOMP) algorithm.
Alternatively, an L1 constraint can be easily added to the optimization problem as a convex variant of a cardinality constraint, in order to induce sparsity on the controller matrices.
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