$L_1$-Norm Regularization for Short-and-Sparse Blind Deconvolution: Point Source Separability and Region Selection

DOI10.1137/21M144904XOpenAlexW4291984299MaRDI QIDQ5043735

Weixi Wang, Hui Ji, Ji Li

Publication date: 6 October 2022

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

Full work available at URL: https://doi.org/10.1137/21m144904x

zbMATH Keywords

blind deconvolution\(L_1\) regularizationsparse-and-short structure

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Unnamed Item
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Rapid, robust, and reliable blind deconvolution via nonconvex optimization
• Blind Deconvolution Using Convex Programming
• Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
• Removing camera shake from a single photograph
• Sparsity and incoherence in compressive sampling
• Identifiability and Stability in Blind Deconvolution Under Minimal Assumptions
• Framelet-Based Blind Motion Deblurring From a Single Image
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
• Phase Retrieval via Matrix Completion

This page was built for publication: $L_1$-Norm Regularization for Short-and-Sparse Blind Deconvolution: Point Source Separability and Region Selection