Lifting for Blind Deconvolution in Random Mask Imaging: Identifiability and Convex Relaxation
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Publication:3454489
DOI10.1137/141002165zbMath1330.94006arXiv1501.00046OpenAlexW1953819449MaRDI QIDQ3454489
Sohail Bahmani, Justin Romberg
Publication date: 25 November 2015
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1501.00046
Convex programming (90C25) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Inverse problems in linear algebra (15A29)
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Uses Software
Cites Work
- Unnamed Item
- Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion
- Hanson-Wright inequality and sub-Gaussian concentration
- A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
- Weak convergence and empirical processes. With applications to statistics
- Local minima and convergence in low-rank semidefinite programming
- Phase retrieval from coded diffraction patterns
- PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming
- Coded Hyperspectral Imaging and Blind Compressive Sensing
- Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices
- Blind Deconvolution Using Convex Programming
- Non-asymptotic theory of random matrices: extreme singular values
- Recovering Low-Rank Matrices From Few Coefficients in Any Basis
- A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing
- A Simpler Approach to Matrix Completion