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Compressed Sensing With General Frames via Optimal-Dual-Based $\ell _{1}$-Analysis

https://doi.org/10.1109/tit.2012.2191612
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31/31 checkable references clean · checked 2026-07-25

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The 31 checked references that resolve
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Compressed Sensing and Redundant Dictionaries
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Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
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Stable signal recovery from incomplete and inaccurate measurements
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Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
resolves10.1109/TIT.2005.862083
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
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Compressed sensing with coherent and redundant dictionaries
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Atomic Decomposition by Basis Pursuit
resolves10.1007/978-0-8176-8224-8_5
Frames in Hilbert Spaces
resolves10.1109/TIT.2006.871582
Compressed sensing
resolves10.1109/TIT.2005.860430
Stable recovery of sparse overcomplete representations in the presence of noise
resolves10.1080/01630569508816668
On general frame decompositions
resolves10.1016/0041-5553(67)90040-7
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
resolves10.1137/100810447
New and Improved Johnson–Lindenstrauss Embeddings via the Restricted Isometry Property
resolves10.1017/CBO9780511804441
Convex Optimization
resolves10.1090/S0025-5718-08-02189-3
Linearized Bregman iterations for compressed sensing
resolves10.1109/78.258082
Matching pursuits with time-frequency dictionaries
resolves10.1137/060657704
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
resolves10.1109/TIT.2009.2021377
On Recovery of Sparse Signals Via $\ell _{1}$ Minimization
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Split Bregman Methods and Frame Based Image Restoration
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NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
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Shifting Inequality and Recovery of Sparse Signals
resolves10.1007/s00365-007-9003-x
A Simple Proof of the Restricted Isometry Property for Random Matrices
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Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)
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A note on guaranteed sparse recovery via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math>-minimization
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Analysis versus synthesis in signal priors
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The Split Bregman Method for L1-Regularized Problems
resolves10.1090/conm/026/737400
Extensions of Lipschitz mappings into a Hilbert space
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Continuous and Discrete Wavelet Transforms
The 3 references without a DOI — listed, not checked
no DOI — not checkedref30
no DOI — not checkedCompressive sampling
no DOI — not checkedref24
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