Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration

Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration. These methods generally follow the nonlocal filtering processing chain, aiming at circumventing the staircase effect and pres...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 2; s. 826 - 840
Hlavní autori:	Kang, Jian, Hong, Danfeng, Liu, Jialin, Baier, Gerald, Yokoya, Naoto, Demir, Begum
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Coding Convolution Convolutional codes Convolutional dictionary learning Dictionaries Encoding Image restoration Interferometry Machine learning Noise reduction nonlocal filtering Phase variations Restoration SAR interferometry (InSAR) Source code sparse coding (SC)
ISSN:	2162-237X, 2162-2388, 2162-2388
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