A low-rank and joint-sparsity model for hyper-spectral radio-interferometric imaging
A low-rank and joint-sparsity model for hyper-spectral radio-interferometric imaging
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DOI：
10.1109/eusipco.2016.7760276
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Abdullah Abdulaziz;A. Dabbech;Alexandru Onose;Y. Wiaux
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文献类型：
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作者：
Abdullah Abdulaziz;A. Dabbech;Alexandru Onose;Y. Wiaux
随着下一代无线电干涉望远镜的出现，如平方公里阵列，需要新的信号处理方法来从极端数量的高光谱数据中提供预期的成像分辨率和灵敏度。在此背景下，我们提出了一个通用的非参数低阶联合稀疏图像模型，用于相关宽带反问题的正则化。我们提出了一个凸优化问题，并建议使用一个有效的算法求解器。所提出的优化任务只需要一个调整参数，即低阶稀疏约束和联合稀疏约束之间的相对权重。我们的初步模拟表明，该模型在单独的单波段成像以及最近推广的其他利用凸优化的非参数宽带模型方面具有优越的性能。
With the advent of the next-generation radio-interferometric telescopes, like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, we propose a generic non-parametric low-rank and joint-sparsity image model for the regularisation of the associated wide-band inverse problem. We pose a convex optimisation problem and propose the use of an efficient algorithmic solver. The proposed optimisation task requires only one tuning parameter, namely the relative weight between the low-rank and joint-sparsity constraints. Our preliminary simulations suggest superior performance of the model with respect to separate single band imaging, as well as to other recently promoted non-parametric wide-band models leveraging convex optimisation.