2022/03/10 by Yaghoub Rahimi, Sung Ha Kang, Rahimi, Yaghoub +3
Computer Science · Engineering · #49M20 #49N45 #65F50 #65K10 #90C90 #Blind Source Separation Techniques #FOS: Electrical engineering #FOS: Mathematics #Image and Signal Denoising Methods #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.05125
openalex publication_date 2022/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Motivated by re-weighted ℓ1 approaches for sparse recovery, we propose a lifted ℓ1 (LL1) regularization which is a generalized form of several popular regularizations in the literature. By exploring such connections, we discover there are two types of lifting functions which can guarantee that the proposed approach is equivalent to the ℓ0 minimization. Computationally, we design an efficient algorithm via the alternating direction method of multiplier (ADMM) and establish the convergence for an unconstrained formulation. Experimental results are presented to demonstrate how this generalization improves sparse recovery over the state-of-the-art.