Quantized Compressed Sensing: A Survey
From MaRDI portal
Publication:3296175
DOI10.1007/978-3-319-73074-5_2zbMath1450.94014OpenAlexW2968828697MaRDI QIDQ3296175
Publication date: 7 July 2020
Published in: Applied and Numerical Harmonic Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-73074-5_2
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Research exposition (monographs, survey articles) pertaining to information and communication theory (94-02)
Related Items
A unified approach to uniform signal recovery from nonlinear observations ⋮ Robust one-bit compressed sensing with partial circulant matrices ⋮ Memoryless scalar quantization for random frames ⋮ New challenges in covariance estimation: multiple structures and coarse quantization ⋮ Covariance estimation under one-bit quantization
Cites Work
- A mathematical introduction to compressive sensing
- Dimensionality reduction with subgaussian matrices: a unified theory
- Democracy in action: quantization, saturation, and compressive sensing
- Distributed noise-shaping quantization. I: Beta duals of finite frames and near-optimal quantization of random measurements
- Uniform recovery of fusion frame structured sparse signals
- Two observations regarding embedding subsets of Euclidean spaces in normed spaces
- Approximating a bandlimited function using very coarsely quantized data: a family of stable sigma-delta modulators of arbitrary order
- Improved bounds for sparse recovery from subsampled random convolutions
- Sobolev duals for random frames and \(\varSigma \varDelta \) quantization of compressed sensing measurements
- Dimension reduction by random hyperplane tessellations
- Recovering low-rank matrices from binary measurements
- Quantized compressed sensing for random circulant matrices
- New analysis of manifold embeddings and signal recovery from compressive measurements
- Low rank tensor recovery via iterative hard thresholding
- Quantization of compressive samples with stable and robust recovery
- One-bit compressed sensing with non-Gaussian measurements
- Learning without Concentration
- Noise-Shaping Quantization Methods for Frame-Based and Compressive Sampling Systems
- One-Bit Compressed Sensing by Linear Programming
- Suprema of Chaos Processes and the Restricted Isometry Property
- Error Decay of (almost) Consistent Signal Estimations from Quantized Gaussian Random Projections
- One-Bit Compressive Sensing With Norm Estimation
- A Quantized Johnson–Lindenstrauss Lemma: The Finding of Buffon’s Needle
- Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
- Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach
- An optimal family of exponentially accurate one-bit Sigma-Delta quantization schemes
- Quantization and Compressive Sensing
- On sparse reconstruction from Fourier and Gaussian measurements
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Compressive Sensing by Random Convolution
- Dithered quantizers
- Quantized overcomplete expansions in IR/sup N/: analysis, synthesis, and algorithms
- One-bit sigma-delta quantization with exponential accuracy
- The Restricted Isometry Property of Subsampled Fourier Matrices
- Small Width, Low Distortions: Quantized Random Embeddings of Low-complexity Sets
- Time for dithering: fast and quantized random embeddings via the restricted isometry property
- Flavors of Compressive Sensing
- High-Dimensional Probability
- On the Gap Between Restricted Isometry Properties and Sparse Recovery Conditions
- Quantization
- Spectral Algorithms for Tensor Completion
- Sigma-Delta quantization of sub-Gaussian frame expansions and its application to compressed sensing
- Fast Binary Embeddings and Quantized Compressed Sensing with Structured Matrices
- One-bit compressive sensing of dictionary-sparse signals
- An Improved Estimate in the Restricted Isometry Problem
- Stable signal recovery from incomplete and inaccurate measurements
- Compressed sensing
