Deblurring and Sparse Unmixing of Hyperspectral Images Using Multiple Point Spread Functions

DOI10.1137/140980478zbMath1325.94029OpenAlexW1925324463MaRDI QIDQ3447458

James G. Nagy, Robert J. Plemmons, Sebastian Berisha

Publication date: 30 October 2015

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/6f473ab886c8a75fc1cff603f10560160d5560c2

zbMATH Keywords

ADMMpreconditioningleast squaresKronecker productimage deblurringhyperspectral imaging

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Preconditioners for iterative methods (65F08)

Related Items (2)

Iterative optimal solutions of linear matrix equations for hyperspectral and multispectral image fusing ⋮ Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis

Cites Work

• Coupled segmentation and denoising/deblurring models for hyperspectral material identification
• A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing
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