Tight-frame-like analysis-sparse recovery using nontight sensing matrices
DOI10.1137/23M1625846zbMATH Open1545.94035MaRDI QIDQ6587659
Kartheek Kumar Reddy Nareddy, Chandra Sekhar Seelamantula, Abijith Jagannath Kamath
Publication date: 14 August 2024
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
tight framesrestricted isometry propertysparse signal recoveryback-projection losscompressed sensing image recoverydeep-unfolded networks
Ill-posedness and regularization problems in numerical linear algebra (65F22) Convex programming (90C25) Nonlinear programming (90C30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Iterative numerical methods for linear systems (65F10)
Cites Work
- Title not available (Why is that?)
- Nearly unbiased variable selection under minimax concave penalty
- Nonlinear total variation based noise removal algorithms
- A mathematical introduction to compressive sensing
- Compressed sensing with coherent tight frames via \(l_q\)-minimization for \(0 < q \leq 1\)
- The road to deterministic matrices with the restricted isometry property
- Compressed sensing with coherent and redundant dictionaries
- Sparsest solutions of underdetermined linear systems via \( \ell _q\)-minimization for \(0<q\leqslant 1\)
- Grassmannian frames with applications to coding and communication
- The art of frame theory
- A preconditioning approach for improved estimation of sparse polynomial chaos expansions
- Data-driven tight frame construction and image denoising
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Equiangular lines
- An efficient augmented Lagrangian method with applications to total variation minimization
- Theory and applications of compressed sensing
- A majorize-minimize subspace approach for \(\ell_2-\ell_0\) image regularization
- Generalized alternating projection for weighted-\(\ell_{2,1}\) minimization with applications to model-based compressive sensing
- Construction of Incoherent Unit Norm Tight Frames With Application to Compressed Sensing
- NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
- On a generalization of the iterative soft-thresholding algorithm for the case of non-separable penalty
- Hard Thresholding Pursuit: An Algorithm for Compressive Sensing
- The Split Bregman Method for L1-Regularized Problems
- Stability of Over-Relaxations for the Forward-Backward Algorithm, Application to FISTA
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Greed is Good: Algorithmic Results for Sparse Approximation
- Designing structured tight frames via an alternating projection method
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
- Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
- Sparse and Redundant Representations
- Compressed Sensing and Redundant Dictionaries
- Iterated Hard Shrinkage for Minimization Problems with Sparsity Constraints
- Matrix Analysis
- Atomic Decomposition by Basis Pursuit
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Optimized Projections for Compressed Sensing
- Bayesian Compressive Sensing
- High-Resolution Radar via Compressed Sensing
- Spatial Compressive Sensing for MIMO Radar
- Smoothing and Decomposition for Analysis Sparse Recovery
- Fienup Algorithm With Sparsity Constraints: Application to Frequency-Domain Optical-Coherence Tomography
- Modulated Unit-Norm Tight Frames for Compressed Sensing
- $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
- Compressive Sensing via Nonlocal Low-Rank Regularization
- Image Restoration by Iterative Denoising and Backward Projections
- AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
- Sparse Regularization via Convex Analysis
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Packing Lines, Planes, etc.: Packings in Grassmannian Spaces
- Matching pursuits with time-frequency dictionaries
- Vector Approximate Message Passing
- Trainable ISTA for Sparse Signal Recovery
- Minimization of $\ell_{1-2}$ for Compressed Sensing
- Model-Based Compressive Sensing
- Analysis versus synthesis in signal priors
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- On the Convergence Rate of Projected Gradient Descent for a Back-Projection Based Objective
- Compressed sensing
This page was built for publication: Tight-frame-like analysis-sparse recovery using nontight sensing matrices
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6587659)
