Sparse Wavelet Representations of Spatially Varying Blurring Operators

DOI10.1137/151003465zbMath1381.94017arXiv1404.1023OpenAlexW2203699045MaRDI QIDQ2797768

Paul Escande, Pierre Weiss

Publication date: 31 March 2016

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

Full work available at URL: https://arxiv.org/abs/1404.1023

zbMATH Keywords

image deblurringwavelet compressionintegral operator approximationspatially varying blurwindowed convolution

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical methods for integral equations (65R20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Linear operator approximation theory (47A58) Integral operators (47G10) Numerical methods for ill-posed problems for integral equations (65R30) Pseudodifferential operators (47G30)

Related Items (8)

Block delayed Majorize-Minimize subspace algorithm for large scale image restoration ^* ⋮ Accelerating \(\ell^1\)-\(\ell^2\) deblurring using wavelet expansions of operators ⋮ Convergence rates of accelerated proximal gradient algorithms under independent noise ⋮ Constraint matrix factorization for space variant PSFs field restoration ⋮ Approximation of integral operators using product-convolution expansions ⋮ A phase model using the Huber norm for estimating point spread function under frozen flow hypothesis ⋮ A scalable estimator of sets of integral operators ⋮ ADMM in Krylov Subspace and Its Application to Total Variation Restoration of Spatially Variant Blur

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