Generalized sampling reconstruction from Fourier measurements using compactly supported shearlets

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• 作者：Jackie Ma ^{ma@math.tu-berlin.de}
• 关键词：Sampling ; Shearlets ; Magnetic resonance imaging ; Sparse reconstructions
• 刊名：Applied and Computational Harmonic Analysis
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：42
• 期：2
• 页码：294-318
• 全文大小：1266 K
• 卷排序：42

文摘

In this paper we study the general reconstruction of a compactly supported function from its Fourier coefficients using compactly supported shearlet systems. We assume that only finitely many Fourier samples of the function are accessible and based on this finite collection of measurements an approximation is sought in a finite dimensional shearlet reconstruction space. We analyze this sampling and reconstruction process by a recently introduced method called generalized sampling. In particular by studying the stable sampling rate of generalized sampling we then show stable recovery of the signal is possible using an almost linear rate. Furthermore, we compare the result to the previously obtained rates for wavelets.