A new non-convex regularized sparse reconstruction algorithm for compressed sensing magnetic resonance image recovery

作者：Yin, Xiangjun; Wang, Linyu; Yue, Huihui^*; Xiang, Jianhong

来源：Progress in Electromagnetics Research C, 2018, 87: 241-253.

DOI：10.2528/pierc18072101

• Compressive sensing
• L0-norm minimizations
• Numerical experiments
• Reconstruction accuracy
• Regularization mechanism
• Sparse reconstruction
• State of the art
• Under-determined

Compressed sensing (CS) relies on the sparse priorin posed on the signal to solve the ill-posed recovery problem in an under-determined linear system (ULS). Motivated by the theory, this paper proposes a new algorithm called regularized re-weighted inverse trigonometric smoothed function approximating L0-norm minimization (RRITSL0) algorithm, where the inverse trigonometric (IT) function, iteratively re-weighted scheme and regularization mechanism constitute the core of the proposed RRITSL0 algorithm. Compared with other state-of-the-art functions, our proposed IT function cluster can better approximate the L0-norm, thus improving the reconstruction accuracy. And the new re-weighted scheme we adopted can promote sparsity and speed up convergence. Moreover, the regularization mechanism makes the RRITSL0 algorithm more robust against noise. The performance of the proposed algorithm is verified via numerical experiments with additive noise. Furthermore, the experiments prove the superiority of the RRITSL0 algorithm in magnetic resonance (MR) image recovery.

• 出版日期2018
• 单位哈尔滨工程大学