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详细介绍了图像稀疏分解思想在数据修复方面的应用。给出了较为详细的理论依据，以及简单的实例介绍-Details of the image sparse decomposition ideas in the application of data recovery. Gives a more detailed theoretical basis, as well as a simple example to illustrate
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`1-magic : Recovery of Sparse Signals
via Convex Programming
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In this paper, we propose a novel compressive
sensing (CS) based approach for sparse target counting and
positioning in wireless sensor networks. While this is not the
first work on applying CS to count and localize targets, it
is the first t
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用于混响背景语音分离的结构稀疏模型(Strutured sparisty model)方法-To further tackle the ambiguity
of the reflection ratios, we propose a novel formulation of the
reverberation model and estimate the absorption coefficients
through a convex optimization exploiting
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联合稀疏恢复的Swarm算法，在基本particle swarm optimization (PSO)算法基础上进行改进-Inspired by particle swarm
optimization (PSO) algorithm and some sparse recovery algorithms,
a novel swarm intelligence algorithm called M-SISR is
proposed to solve the problem. In M-
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