Lifting for Blind Deconvolution in Random Mask Imaging: Identifiability and Convex Relaxation

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

zbMATH Keywords

liftingblind deconvolutionnuclear norm minimizationcoded mask imaging

Mathematics Subject Classification ID

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)

Related Items

Efficient Identification of Butterfly Sparse Matrix Factorizations, Near-optimal estimation of simultaneously sparse and low-rank matrices from nested linear measurements, Non-blind and blind deconvolution under Poisson noise using fractional-order total variation, Estimation from nonlinear observations via convex programming with application to bilinear regression, Self-Calibration and Bilinear Inverse Problems via Linear Least Squares, Multilinear Compressive Sensing and an Application to Convolutional Linear Networks

Uses Software

• SDPLR
• PSF Generator
• PhaseLift

Cites Work

• Unnamed Item
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