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A novel multi-dictionary framework with global sensing matrix design for compressed sensing

DonghaiBao 添加于 2018/5/29 15:07:28  370次阅读 | 0次推荐 | 0个评论

In this paper, a new compressed sensing (CS) system is proposed to reduce computational burden of dic- tionary learning and improve reconstruction accuracy. The proposed CS system employs a novel frame- work which contains multiple dictionaries. In multi-dictionary framework, the whole training dataset is divided into multiple subdatasets for optimizing multiple dictionaries. Dictionary learning process can be accelerated due to the parallel computation and the reduction of training dataset size. Each dictionary can get an image (called snapshot) independently with the same measurements in the image reconstruc- tion process. These snapshots will be fused to be one image with averaging strategy. In order to keep the measurement size of the proposed CS system same as that of traditional CS system and improve re- construction accuracy, a new method of designing global sensing matrix for multi-dictionary framework is also explored. Experiments demonstrate the effectiveness of new framework and the method to de- sign global sensing matrix. Compared with other CS systems, the proposed CS system shows a superior performance for real images.

作 者:Jiajun Ding , Donghai Bao , Qingpei Wang, Xiongxiong He, Huang Bai, Sheng Li
期刊名称: Signal Processing
期卷页: 第152卷 第期 69-78页
学科领域:信息科学 » 电子学与信息系统 » 信号理论与信号处理
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关键词: Compressed sensing, Multi-dictionary framework, Global sensing matrix, Image processing
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