Reconstructing Image From Noisy Radon Projections Using Shearlet Transform

2021 ◽  
Author(s):  
Tajedin Derikvand ◽  
Rajab Ali Kamyabi Gol
Keyword(s):  
Shearlet Transform ◽  
Radon Projections

Image de-noising algorithm based on Shearlet transform

2010 ◽  
Vol 30 (6) ◽  
pp. 1562-1564 ◽  
Author(s):  
Hai-zhi HU ◽  
Hui SUN ◽  
Cheng-zhi DENG ◽  
Xi CHEN ◽  
Zhi-hua LIU ◽  
...  
Keyword(s):  
Shearlet Transform

Local Spatio-Temporal Representation Using the 3D Shearlet Transform

2018 ◽  
Vol 17 (1) ◽  
pp. 57-72
Author(s):  
Damiano Malafronte ◽  
Ernesto De Vito ◽  
Francesca Odone
Keyword(s):  
Shearlet Transform ◽  
Temporal Representation ◽  
Spatio Temporal

scholarly journals Approximation and Reconstruction from Attenuated Radon Projections

2007 ◽  
Vol 45 (1) ◽  
pp. 108-132 ◽  
Author(s):  
Yuan Xu ◽  
Oleg Tischenko ◽  
Christoph Hoeschen
Keyword(s):  
Radon Projections

Multicomponent microseismic data denoising by 3D shearlet transform

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. A45-A51 ◽  
Author(s):  
Chao Zhang ◽  
Mirko van der Baan
Keyword(s):  
Noise Suppression ◽  
Random Noise ◽  
Real Data ◽  
Fracture Initiation ◽  
Multidimensional Data ◽  
Data Sets ◽  
Denoising Method ◽  
Shearlet Transform ◽  
Microseismic Data ◽  
Significant Step

The low-magnitude microseismic signals generated by fracture initiation are generally buried in strong background noise, which complicates their interpretation. Thus, noise suppression is a significant step. We have developed an effective multicomponent, multidimensional microseismic-data denoising method by conducting a simplified polarization analysis in the 3D shearlet transform domain. The 3D shearlet transform is very competitive in dealing with multidimensional data because it captures details of signals at different scales and orientations, which benefits signal and noise separation. We have developed a novel processing strategy based on a signal-detection operator that can effectively identify signal coefficients in the shearlet domain by taking the correlation and energy distribution of 3C microseismic signals into account. We perform tests on synthetic and real data sets and determine that the proposed method can effectively remove random noise and preserve weak signals.

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