Learning a Gaussian Mixture for Sparsity Regularization in Inverse
Problems
Learning a Gaussian Mixture for Sparsity Regularization in Inverse
Problems
In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution. This approach is grounded on the a priori assumption that the unknown can be appropriately represented in a basis with a limited number of significant components, while most coefficients …