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Adaptive Compressive Imaging for Object Reconstruction

机译:对象重建的自适应压缩成像

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Static Feature-specific imaging (SFSI) employing a fixed/static measurement basis has been shown to achieve superior reconstruction performance to conventional imaging under certain conditions.~(1-5) In this paper, we describe an adaptive FSI system in which past measurements inform the choice of measurement basis for future measurements so as to maximize the reconstruction fidelity while employing the fewest measurements. An algorithm to implement an adaptive FSI system for principle component (PC) measurement basis is described. The resulting system is referred to as a PC-based adaptive FSI (AFSI) system. A simulation study employing the root mean squared error (RMSE) metric to quantify the reconstruction fidelity is used to analyze the performance of the PC-based AFSI system. We observe that the AFSI system achieves as much as 30% lower RMSE compared to a SFSI system.
机译:采用固定/静态测量的静态特定成像(SFSI)已被证明在某些条件下对常规成像进行了卓越的重建性能。〜(1-5)在本文中,我们描述了一种过去测量的自适应FSI系统为未来测量的选择提供测量基础,以便在采用最少的测量时最大限度地提高重建保真度。描述了用于实现原理组件(PC)测量的自适应FSI系统的算法。得到的系统被称为基于PC的自适应FSI(AFSI)系统。采用具有根均方误差(RMSE)度量来量化重建保真度的仿真研究用于分析基于PC的AFSI系统的性能。我们观察到,与SFSI系统相比,AFSI系统实现了更低的RMSE。

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