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An improved algorithm for basis pursuit problem and its applications
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DOI：10.1016/j.amc.2019.02.073.png)
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We propose an algorithm for solving the basis pursuit problem min(u is an element of Cn){parallel to u parallel to(1) : Au =f}. Our starting motivation is the algorithm for compressed sensing, proposed by Qiao, Li and Wu, which is based on linearized Bregman iteration with generalized inverse. Qiao, Li and Wu defined new algorithm for solving the basis pursuit problem in compressive sensing using a linearized Bregman iteration and the iterative formula of linear convergence for computing the matrix generalized inverse. In our proposed approach, we combine a partial application of the Newton's second order iterative scheme for computing the generalized inverse with the Bregman iteration. Our scheme takes lesser computational time and gives more accurate results in most cases. The effectiveness of the proposed scheme is illustrated in two applications: signal recovery from noisy data and image deblurring. (C) 2019 Elsevier Inc. All rights reserved.
Keyword:
Generalized inverse
Linearized Bregman iteration
Compressive sensing
Sparse solution
Signal recovery
Image deblurring
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期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
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