Blind Deconvolution by a Steepest Descent Algorithm on a Quotient Manifold

DOI10.1137/17M1151390zbMath1425.65069arXiv1710.03309OpenAlexW2964249688MaRDI QIDQ5236625

Wen Huang, Paul E. Hand

Publication date: 9 October 2019

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.03309

zbMATH Keywords

nonconvex optimizationblind deconvolutionimage deblurringRiemannian optimizationquotient manifoldlow-rank matrices

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Programming in abstract spaces (90C48) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Sampling theory in information and communication theory (94A20)

Related Items (7)

Analysis of Asymptotic Escape of Strict Saddle Sets in Manifold Optimization ⋮ A limited-memory Riemannian symmetric rank-one trust-region method with a restart strategy ⋮ Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution ⋮ Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs ⋮ Adaptive trust-region method on Riemannian manifold ⋮ Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval ⋮ Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold

Uses Software

• FFTW
• SDPLR
• RTRMC
• Flavia
• ROPTLIB

Cites Work

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• Blind Image Deconvolution

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