A Note on Compressed Sensing of Structured Sparse Wavelet Coefficients From Subsampled Fourier Measurements
| Autor: | Anders C. Hansen, Ben Adcock, Bogdan Roman |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Discrete wavelet transform
Mathematical optimization Applied Mathematics Stationary wavelet transform Second-generation wavelet transform Wavelet transform 020206 networking & telecommunications Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Haar wavelet Functional Analysis (math.FA) Wavelet packet decomposition Mathematics - Functional Analysis Wavelet Signal Processing FOS: Mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering Harmonic wavelet transform Algorithm Mathematics |
| Zdroj: | IEEE Signal Processing Letters. 23:732-736 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2016.2550101 |
| Popis: | This note complements the paper "The quest for optimal sampling: Computationally efficient, structure-exploiting measurements for compressed sensing" [2]. Its purpose is to present a proof of a result stated therein concerning the recovery via compressed sensing of a signal that has structured sparsity in a Haar wavelet basis when sampled using a multilevel-subsampled discrete Fourier transform. In doing so, it provides a simple exposition of the proof in the case of Haar wavelets and discrete Fourier samples of more general result recently provided in the paper "Breaking the coherence barrier: A new theory for compressed sensing" [1]. Comment: 8 pages, companion paper |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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