Practical Compressed Sensing: Modern data acquisition and signal processing
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Summary
Inspired by the need for a fast method to solve reconstruction problems for the RMPI, two efficient large-scale optimization methods are developed that are applicable to a wide range of other problems, such as image denoising and deblurring, MRI reconstruction, and matrix completion (including the famous Netflix problem).
- Type
- dissertation
- Published
- 2011-01-01
- Cited by
- 86
- References
- 232
- OpenAlex
- https://openalex.org/W189254822
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:29917877
Keywords
Compressed sensing, Computer science, Deblurring, Integrator, Smoothing
References
- An Introduction To Compressive Sampling
- Practical Signal Recovery from Random Projections
- Accurate time delay estimation based passive localization
- Convex Analysis and Monotone Operator Theory in Hilbert Spaces
- A Wavelet Tour of Signal Processing : The Sparse Way
- The Dantzig selector: Statistical estimation when P is much larger than n
- Design of Multi-Bit Delta-SIGMA A/D Converters
- Improved sparse recovery thresholds with two-step reweighted ℓ1 minimization
- Problem Complexity and Method Efficiency in Optimization
- Review of 'Numerical Optimization' by Bonnans, Gilbert, Lemaréchal and Sagastizabal
- Primal-Dual Interior-Point Methods
- Quantum state tomography via compressed sensing.
- Combinatorial Group Testing and Its Applications
- Wavelet Methods in Statistics with R
- Convex analysis and minimization algorithms
- Combining geometry and combinatorics: A unified approach to sparse signal recovery
- Interior-point polynomial algorithms in convex programming
- Algorithmic linear dimension reduction in the l_1 norm for sparse vectors
- Noisy signal recovery via iterative reweighted L1-minimization
- A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms
Cited by
- Compressed Sensing of Multichannel EEG Signals: The Simultaneous Cosparsity and Low-Rank Optimization
- Interference Cancellation in Wideband Receivers using Compressed Sensing
- Compressed Sensing Receivers: Theory, Design, and Performance Limits
- Sparse Ultra Wide Band Radar imaging in a locally adapting matching pursuit (LAMP) framework
- Using synchronism pulse to improve A2I implementations
- Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing
- Segment-sliding reconstruction of pulsed radar echoes with sub-Nyquist sampling
- Spectrum cartography using quantized observations
- Performance Limits of Segmented Compressive Sampling: Correlated Measurements Versus Bits
- Making Do with Less: An Introduction to Compressed Sensing
- VLSI Design of a Monolithic Compressive-Sensing Wideband Analog-to-Information Converter
- A Compressed Sensing Parameter Extraction Platform for Radar Pulse Signal Acquisition
- A Compressive Sensing Based Analysis of Anomalies in Generalized Linear Models
- A Low-Power Compressive Sampling Time-Based Analog-to-Digital Converter
- Real-time and low power embedded ℓ1-optimization solver design
- Electrical bioimpedance spectroscopy in time-variant systems: Is undersampling always a problem?
- Compression Limits for Random Vectors with Linearly Parameterized Second-Order Statistics
- Online spectrum cartography via quantized measurements
- Ultra-low-power ECG front-end design based on compressed sensing
- Sensitivity simulation of compressed sensing based EW receiver using orthogonal matching pursuit algorithm
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