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Spectrum reconstruction for filter-array spectrum sensor using sparse representation

机译:基于稀疏表示的滤波器阵列频谱传感器频谱重构

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In recent years, miniature spectrometers have been found useful in many applications to resolve spectrum signature of objects or materials. In this paper, algorithms for filter-array spectrum sensor to realize miniature spectrometers are investigated. Conventionally, the filter-array spectrum sensor can be modeled as an over-determined problem, and the spectrum can be reconstructed by solving a set of linear equations. On the contrary, we model the spectrum reconstruction process as an under-determined problem, and bring up the concept of template-selection by sparse representation. L1-minimization algorithm is tested to achieve a high reconstruction resolution. Simulation results show superior quality of spectrum reconstruction can be made possible from this under-determined approach
机译:近年来,发现微型光谱仪在许多应用中可用于解决物体或材料的光谱特征。本文研究了滤波器阵列频谱传感器实现微型光谱仪的算法。传统上,可以将滤波器阵列频谱传感器建模为一个超定问题,并且可以通过求解一组线性方程来重建频谱。相反,我们将频谱重建过程建模为一个不确定的问题,并提出了基于稀疏表示的模板选择概念。测试了L1最小化算法以实现较高的重建分辨率。仿真结果表明,利用这种欠定的方法可以实现出色的频谱重建质量

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