Fast Dictionary Learning for High-Dimensional Seismic Reconstruction

A sparse dictionary is more adaptive than a sparse fixed-basis transform since it can learn the features directly from the input data in a data-driven way. However, learning a sparse dictionary is time-consuming because a large number of iterations are required in order to obtain the dictionary atom...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 59; číslo 8; s. 7098 - 7108
Hlavní autori: Wang, Hang, Chen, Wei, Zhang, Quan, Liu, Xingye, Zu, Shaohuan, Chen, Yangkang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Atomic properties
Computational efficiency
Computer applications
Computing costs
Decomposition
Dictionaries
Dictionary learning (DL)
Encoding
Glossaries
high-dimensional problem
Image reconstruction
Learning
Mathematical model
Seismic data
seismic data reconstruction
signal processing
Singular value decomposition
Sparse matrices
Transforms
ISSN:0196-2892, 1558-0644
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Shrnutí:A sparse dictionary is more adaptive than a sparse fixed-basis transform since it can learn the features directly from the input data in a data-driven way. However, learning a sparse dictionary is time-consuming because a large number of iterations are required in order to obtain the dictionary atoms that best represent the features of input data. The computational cost becomes unaffordable when it comes to high-dimensional problems, e.g., 3-D or even 5-D applications. We propose an efficient high-dimensional dictionary learning (DL) method by avoiding the singular value decomposition (SVD) calculation in each dictionary update step that is required by the classic <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-singular value decomposition (KSVD) algorithm. Besides, due to the special structure of the sparse coefficient matrix, it requires a much less expensive sparse coding process. The overall computational efficiency of the new DL method is much higher, while the results are still comparable or event better than those from the traditional KSVD method. We apply the proposed method to both 3-D and 5-D seismic data reconstructions and demonstrate successful and efficient performance.
Bibliografia:ObjectType-Article-1
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3030740