基于壓縮感知的加速前向后向匹配追蹤算法

王鋒; 孫桂玲; 張健平; 何靜飛

doi:10.11999/JEIT151422

基于壓縮感知的加速前向后向匹配追蹤算法

doi: 10.11999/JEIT151422

1.
(南開大學(xué)電子信息與光學(xué)工程學(xué)院天津 300350) ②(上海交通大學(xué)電子信息與電氣工程學(xué)院上海 200030)

基金項(xiàng)目:

國家自然科學(xué)基金(61171140)，高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(20130031110032)

計(jì)量
- 文章訪問數(shù): 1684
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-12-14
- 修回日期: 2016-05-05
- 刊出日期: 2016-10-19

Acceleration Forward-backward Pursuit Algorithm Based on Compressed Sensing

1.
(College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China)
2.
(School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China)

Funds:

The National Natural Science Foundation of China (61171140), The Doctoral Program of Higher Education (20130031110032)

摘要

摘要: 前向后向匹配追蹤(FBP)算法作為一個(gè)新穎的兩階段貪婪逼近算法，因?yàn)檩^高的重構(gòu)精度和不需要稀疏度作為先驗(yàn)信息的特點(diǎn)，受到了人們的廣泛關(guān)注。然而，F(xiàn)BP算法必須運(yùn)行更多的時(shí)間才能得到更高的精度。鑒于此，該文提出加速前向后向匹配追蹤(AFBP)算法。該算法利用每次迭代中候選支撐集的信息，實(shí)現(xiàn)對已刪除原子的再次加入，以此減少算法迭代次數(shù)。通過不同非零項(xiàng)分布的稀疏信號和稀疏圖像的仿真結(jié)果表明，相對于FBP算法，該文提出的方案在不降低重構(gòu)精度的同時(shí)，大幅降低了算法運(yùn)行時(shí)間。
- 壓縮感知 /
- 貪婪算法 /
- 前向后向搜索 /
- 稀疏信號重構(gòu)
Abstract: The Forward-Backward Pursuit (FBP) algorithm, a novel two stage greedy approach, receives wide attention due to the high reconstruction accuracy and the feature without prior information of the sparsity. However, FBP has to run more time to get a higher precision. To alleviate this drawback, this paper proposes the Acceleration Forward-Backward Pursuit (AFBP) algorithm based on Compressed Sensing (CS). In order to reduce the number of iterations, the algorithm exploits the information available in the support estimate to add the deleted atoms again. The run time of AFBP is sharply shorter than that of FBP, while the precision of AFBP is not lower than FBP. The efficacy of the proposed scheme is demonstrated by simulations using random sparse signals with different nonzero coefficient distributions and a sparse image.
- Compressed Sensing (CS) /
- Greedy algorithms /
- Forward-backward search /
- Sparse signal reconstruction

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參考文獻(xiàn)(19)

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