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- Title
AFISTA: Accelerated FISTA for sparse signal recovery and compressive sensing.
- Authors
Babapour, Shahab; Lakestani, Mehrdad; Fatholahzadeh, Abolfazl
- Abstract
This paper presents a new fast iterative shrinkage-thresholding algorithm, termed AFISTA. The essential idea is to improve the convergence rate of FISTA using a new continuation strategy leading to a less number of iterations compared to FISTA. The convergence theorem of the AFISTA is proposed. In order to further accelerate the AFISTA method, it is equipped with the Barzilai-Borwein (BB) method. Also, for applications with orthogonal sensing matrix A, we proposed a specialized version of the AFISTA method. AFISTA is tailored for solving the basis pursuit problem which can be applied successfully on a variety of problems arising in signal and image processing issues such as sparse signal recovery, signal and image denoising, image restoration, and compressive sensing. To show the efficiency of the method, we compare our results with generalizations of linearized Bregman and fixed - point continuation (FPC) methods in sparse signal recovery applications, with split Bregman method in compressive sensing for sparse MRI and with Gradient projection for sparse reconstruction (GPSR) method in image deconvolution. Numerical results demonstrate that AFISTA overcomes all of the compared methods in convergence rate and some of them in both convergence rate and quality of reconstructed results.
- Subjects
IMAGE reconstruction; THRESHOLDING algorithms; SIGNAL denoising; IMAGE denoising; IMAGE processing; SIGNAL processing
- Publication
Multimedia Tools & Applications, 2021, Vol 80, Issue 13, p20707
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-021-10701-w