Convolutional Sparse Coding Fast Approximation With Application to Seismic Reflectivity Estimation

In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks. Similarly, neural networks perform a given task by learning features of the training dataset. Recently, both data- and model-driven f...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 19
Main Authors:	Pereg, Deborah, Cohen, Israel, Vassiliou, Anthony A.
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation Approximation algorithms Coding Coherence Convolution Convolutional neural network (CNN) convolutional sparse coding (CSC) Data points deep learning Dictionaries Encoding Feature extraction Glossaries Image coding Iterative methods Mathematical analysis Neural networks Receptive field Reflectance seismic inversion Seismic surveys Solvers sparse reflectivity Training Vectors
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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