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Parametric Wavelets: Tight Frames Class

IFAC Proceedings Volumes, 1997
Abstract 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

2018
If \((f_j)_{j\in J}\) 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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Tight junctions: from simple barriers to multifunctional molecular gates

Nature Reviews Molecular Cell Biology, 2016
Ceniz Zihni, Clare Mills, Karl Matter
exaly  

Data-driven tight frame construction and image denoising

Applied and Computational Harmonic Analysis, 2014
Jian-Feng Cai, Hui Ji, Zuowei Shen
exaly  

Multifunctional strands in tight junctions

Nature Reviews Molecular Cell Biology, 2001
Mikio Furuse, Masahiko Itoh
exaly  

Convex Denoising using Non-Convex Tight Frame Regularization

IEEE Signal Processing Letters, 2015
Ankit Parekh, Ivan W Selesnick
exaly  

Iteratively reweighted algorithm for signals recovery with coherent tight frame

Mathematical Methods in the Applied Sciences, 2018
Kaihao Liang
exaly  

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