Reconstruction from convolution random sampling in local shift invariant spaces

2019 ◽  
Vol 35 (12) ◽  
pp. 125008 ◽  
Author(s):  
Yaxu Li ◽  
Jinming Wen ◽  
Jun Xian
2019 ◽  
Vol 17 (02) ◽  
pp. 323-347 ◽  
Author(s):  
Jianbin Yang

Shift-invariant spaces play an important role in approximation theory, wavelet analysis, finite elements, etc. In this paper, we consider the stability and reconstruction algorithm of random sampling in multiply generated shift-invariant spaces [Formula: see text]. Under some decay conditions of the generator [Formula: see text], we approximate [Formula: see text] with finite-dimensional subspaces and prove that with overwhelming probability, the stability of sampling set conditions holds uniformly for all functions in certain compact subsets of [Formula: see text] when the sampling size is sufficiently large. Moreover, we show that this stability problem is connected with properties of the random matrix generated by [Formula: see text]. In the end, we give a reconstruction algorithm for the random sampling of functions in [Formula: see text].


2021 ◽  
Vol 27 (3) ◽  
Author(s):  
Maria Charina ◽  
Vladimir Yu. Protasov

AbstractIn this paper we characterize all subspaces of analytic functions in finitely generated shift-invariant spaces with compactly supported generators and provide explicit descriptions of their elements. We illustrate the differences between our characterizations and Strang-Fix or zero conditions on several examples. Consequently, we depict the analytic functions generated by scalar or vector subdivision with masks of bounded and unbounded support. In particular, we prove that exponential polynomials are indeed the only analytic limits of level dependent scalar subdivision schemes with finitely supported masks.


2008 ◽  
Vol 25 (2) ◽  
pp. 240-265 ◽  
Author(s):  
Brigitte Forster ◽  
Thierry Blu ◽  
Dimitri Van De Ville ◽  
Michael Unser

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