A convergent least-squares regularized blind deconvolution approach

DOI10.1016/j.amc.2015.02.048zbMath1391.94064OpenAlexW1976851246MaRDI QIDQ1636832

Anastasia Cornelio, Marco Prato, Federica Porta

Publication date: 7 June 2018

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11380/1063871

zbMATH Keywords

regularizationleast squares problemsblind deconvolutionoptimization methods

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (4)

Parametric PSF estimation based on recursive SURE for sparse deconvolution ⋮ A Semiblind Regularization Algorithm for Inverse Problems with Application to Image Deblurring ⋮ A cyclic block coordinate descent method with generalized gradient projections ⋮ A block coordinate variable metric linesearch based proximal gradient method

Uses Software

• UNLocBoX

Cites Work

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• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Proximal alternating linearized minimization for nonconvex and nonsmooth problems
• Implementation of an optimal first-order method for strongly convex total variation regularization
• Regularization methods for the solution of inverse problems in solar X-ray and imaging spectroscopy
• New adaptive stepsize selections in gradient methods
• On gradients of functions definable in o-minimal structures
• On semi- and subanalytic geometry
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Preconditioned iterative regularization in Banach spaces
• A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
• A convergent blind deconvolution method for post-adaptive-optics astronomical imaging
• A New Semiblind Deconvolution Approach for Fourier-Based Image Restoration: An Application in Astronomy
• A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
• Proximal Splitting Methods in Signal Processing
• Inexact block coordinate descent methods with application to non-negative matrix factorization
• Analysis of an approximate model for Poisson data reconstruction and a related discrepancy principle
• Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
• Deblurring Images
• A scaled gradient projection method for constrained image deblurring
• Efficient gradient projection methods for edge-preserving removal of Poisson noise
• Two-Point Step Size Gradient Methods
• Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus
• Newton's Method for Large Bound-Constrained Optimization Problems
• Nonnegative image reconstruction from sparse Fourier data: a new deconvolution algorithm
• Nonnegative least-squares image deblurring: improved gradient projection approaches
• Blind and Semi-Blind Deblurring of Natural Images
• Parameter Estimation for Blind and Non-Blind Deblurring Using Residual Whiteness Measures
• Projected Gradient Methods for Nonnegative Matrix Factorization
• Signal Recovery by Proximal Forward-Backward Splitting

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