Name
Title | ||
|---|---|---|
Accelerated Split Bregman Method for Image Compressive Sensing Recovery under Sparse Representation. |
Abstract | ||
|---|---|---|
Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3837/tiis.2016.06.016 | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Compressive sensing,sparse representation,split Bregman method,accelerated split Bregman method,image restoration | Convergence (routing),Pattern recognition,Matrix (mathematics),Computer science,Sparse approximation,Fourier transform,Bregman method,Fast Fourier transform,Artificial intelligence,Image restoration,Compressed sensing | Journal |
Volume | Issue | ISSN |
10 | 6 | 1976-7277 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Gao | 1 | 12 | 2.94 |
| Peng Lan | 2 | 41 | 8.25 |
| Xiaoming Chen | 3 | 301 | 28.67 |
| Li Zhang | 4 | 0 | 0.34 |
| Fenggang Sun | 5 | 0 | 0.68 |