<ul data-eligibleForWebStory="true"><li>In inverse problems, sparsity regularization is used to regularize the solution by assuming that the unknown can be represented with only a few significant components.</li><li>A new probabilistic sparsity prior based on a mixture of degenerate Gaussians is proposed to model sparsity in a versatile manner in this study.</li><li>A neural network is designed as the Bayes estimator for linear inverse problems under the probabilistic sparsity prior framework.</li><li>Supervised and unsupervised training strategies are suggested to estimate the parameters of this neural network.</li><li>The effectiveness of the proposed approach is evaluated against common sparsity-promoting techniques like LASSO, group LASSO, iterative hard thresholding, and sparse coding/dictionary learning.</li><li>Comparison results show that the reconstructions using the new approach consistently have lower mean square error values on 1D datasets compared to traditional techniques.</li></ul>

Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems

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