A Unified Fractional Regularization Framework for Sparse Recovery
This work provides a theoretical unification and improved recovery guarantees for nonconvex sparse regularization, benefiting signal processing and imaging communities.
The paper introduces a unified fractional regularization framework for sparse recovery, establishing theoretical equivalence between different nonconvex regularizers and proving robust recovery under high-coherence matrices. The proposed method consistently outperforms existing approaches in numerical experiments on various sensing matrices and MRI reconstruction.
We propose a unified fractional regularization framework for sparse signal recovery based on the $\ell_1/\ell_p^q$ model. Our main theoretical contribution is the characterization of the equivalence between the first-order stationary points of the $\ell_1/\ell_p^q$ formulation and the subtractive $\ell_1 - α\ell_p$ model, providing a unified perspective on these nonconvex regularizers. In addition, we establish a new sufficient recovery condition under the Restricted Isometry Property (RIP), showing that the framework's robustness even under high-coherence sensing matrices. To solve the resulting problem, we develop a majorization-minimization (MM) algorithm and prove its convergence via the Kurdyka-Lojasiewicz (KL) property. Numerical experiments on different sensing matrices and MRI reconstruction demonstrate that the proposed approach consistently outperforms existing methods.