Multidimensional unstructured sparse recovery via eigenmatrix
This work addresses sparse recovery challenges in signal processing and imaging, but it appears incremental as it extends an existing method to higher dimensions.
The paper tackles multidimensional unstructured sparse recovery problems, such as Fourier inversion and sparse deconvolution, by extending the eigenmatrix approach from one-dimensional to multidimensional settings, with numerical results demonstrating its performance.
This note considers the multidimensional unstructured sparse recovery problems. Examples include Fourier inversion and sparse deconvolution. The eigenmatrix is a data-driven construction with desired approximate eigenvalues and eigenvectors proposed for the one-dimensional problems. This note extends the eigenmatrix approach to multidimensional problems. Numerical results are provided to demonstrate the performance of the proposed method.