Seismic data interpolation and denoising by learning a tensor tight frame

DOI10.1088/1361-6420/aa7773zbMath1411.86004OpenAlexW2624114522MaRDI QIDQ4592696

Lina Liu, Jianwei Ma, Gerlind Plonka-Hoch

Publication date: 8 November 2017

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/1f95fd11428b586e4011c3bd885cd7e790726046

zbMATH Keywords

dictionary learningseismic inversionsparse transformgeophysical data processingdenoising and interploation

Mathematics Subject Classification ID

Seismology (including tsunami modeling), earthquakes (86A15) Inverse problems in geophysics (86A22)

Related Items (1)

A tree-based dictionary learning framework

Cites Work

• Tensor Decompositions and Applications
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Data-driven tight frame construction and image denoising
• Why Simple Shrinkage Is Still Relevant for Redundant Representations?
• A Multilinear Singular Value Decomposition
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration
• Phase retrieval for Fresnel measurements using a shearlet sparsity constraint
• Sparsity and incoherence in compressive sampling

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