Algorithms to Approximately Solve NP Hard Row-Sparse MMV Recovery Problem: Application to Compressive Color Imaging
Algorithms to Approximately Solve NP Hard Row-Sparse MMV Recovery Problem: Application to Compressive Color Imaging
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DOI：
10.1109/jetcas.2012.2212774
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发表时间：
2012-09-01
影响因子：
4.6
通讯作者：
Aboulnasr, Tyseer
中科院分区：
文献类型：
--
作者：
Majumdar, Angshul;Ward, Rabab K.;Aboulnasr, Tyseer
本文研究了行稀疏多测量向量（MMV）恢复问题。这需要求解非确定性多项式（NP）硬优化。而不是近似的NP困难的问题，其凸/非凸代理人在其他研究中所做的，我们提出的技术，直接解决NP困难的问题近似与易处理的算法。这里推导出的算法比我们比较的最先进的凸（谱投影梯度）算法产生更好的恢复率。我们表明，压缩彩色图像重建可以制定为一个MMVrecovery问题与稀疏行，因此可以解决我们提出的方法。重建的图像是更准确的（提高约2dB的峰值信噪比）比以前的技术相比。
This paper addresses the row-sparse multiple measurement vector (MMV) recovery problem. This requires solving a nondeterministic polynomial (NP) hard optimization. Instead of approximating the NP hard problem by its convex/ nonconvex surrogates as is done in other studies, we propose techniques to directly solve the NP hard problem approximately with tractable algorithms. The algorithms derived in here yields better recovery rates than the state-of-the-art convex (spectral projected gradient) algorithm we compared against. We show that the compressive color image reconstruction can be formulated as anMMVrecovery problem with sparse rows and therefore can be solved by our proposed method. The reconstructed images are more accurate (improvement about 2 dB in peak signal-to-noise ratio) than the previous technique compared against.