A Unified Fractional Regularization Framework for Sparse Recovery

We propose a unified fractional regularization framework for sparse signal recovery based on the ℓ1/ℓpq model. This model generalizes several widely used sparsity-promoting regularizers and provides additional flexibility through the parameters p and q. Our main theoretical contribution is the characterization of the equivalence between the first-order stationary points of the ℓ1/ℓpq formulation and the subtractive ℓ1-αℓp model, thereby offering a unified perspective on these nonconvex regularizers. In addition, we establish a new sufficient recovery condition under the Restricted Isometry Property (RIP), which shows that the proposed framework can provide relaxed recovery guarantees and improved robustness. To solve the resulting nonconvex problem, we develop a majorization--minimization (MM) algorithm and prove its convergence by using the Kurdyka--Lojasiewicz (KL) property. Numerical experiments on sparse recovery problems with different sensing matrices and MRI reconstruction demonstrate that the proposed approach outperforms existing methods in recovery accuracy.