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Parametric Wavelets: Tight Frames Class
IFAC Proceedings Volumes, 1997Abstract In this paper, several methods to enlarge by parametrization the families of wavelets are proposed. The main goal is to prove that every remarkable classical wavelet can generate a class of wavelets, each one preserving the main characteristics of the original, but offering more mobility for time-frequency representations. Here, an important
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Variational characterisations of tight frames
2018If \((f_j)_{j\in J}\) is a finite tight frame for \(\mathscr {H}\), then (see Proposition 2.1). \(\sum _{j\in J}\sum _{k\in J} |\langle f_j, f_k\rangle |^2 = {1\over d} \Bigl (\sum _{j\in J}\langle f_j, f_j\rangle \Bigr )^2, \qquad d=\dim (\mathscr {H}).\)
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Variational bimodal image fusion with data-driven tight frame
Information Fusion, 2020Xiaoqun Zhang
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Tight junctions: from simple barriers to multifunctional molecular gates
Nature Reviews Molecular Cell Biology, 2016Ceniz Zihni, Clare Mills, Karl Matter
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Data-driven tight frame construction and image denoising
Applied and Computational Harmonic Analysis, 2014Jian-Feng Cai, Hui Ji, Zuowei Shen
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Multifunctional strands in tight junctions
Nature Reviews Molecular Cell Biology, 2001Mikio Furuse, Masahiko Itoh
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Convex Denoising using Non-Convex Tight Frame Regularization
IEEE Signal Processing Letters, 2015Ankit Parekh, Ivan W Selesnick
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Iteratively reweighted algorithm for signals recovery with coherent tight frame
Mathematical Methods in the Applied Sciences, 2018Kaihao Liang
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