Noise suppression using block-based singular value decomposition filtering

2007 ◽  
Author(s):  
Napa Sae-Bae ◽  
Somkait Udomhunsakul
Keyword(s):  
Singular Value Decomposition ◽  
Noise Suppression ◽  
Singular Value ◽  
Block Based ◽  
Value Decomposition

Local singular value decomposition for signal enhancement of seismic data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. V59-V65 ◽  
Author(s):  
Maïza Bekara ◽  
Mirko Van der Baan
Keyword(s):  
Singular Value Decomposition ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Global Scale ◽  
Signal Enhancement ◽  
Singular Value ◽  
Median Filtering ◽  
Seismic Sections ◽  
Value Decomposition

Singular value decomposition (SVD) is a coherency-based technique that provides both signal enhancement and noise suppression. It has been implemented in a variety of seismic applications — mostly on a global scale. In this paper, we use SVD to improve the signal-to-noise ratio of unstacked and stacked seismic sections, but apply it locally to cope with coherent events that vary with both time and offset. The local SVD technique is compared with [Formula: see text] deconvolution and median filtering on a set of synthetic and real-data sections. Local SVD is better than [Formula: see text] deconvolution and median filtering in removing background noise, but it performs less well in enhancing weak events or events with conflicting dips. Combining [Formula: see text] deconvolution or median filtering with local SVD overcomes the main weaknesses associated with each individual method and leads to the best results.

Block based embedding of encrypted watermarks using singular value decomposition

Optik ◽  
2014 ◽  
Vol 125 (20) ◽  
pp. 6299-6304 ◽  
Author(s):  
Rania A. Ghazy ◽  
Mohammed Amoon ◽  
Hanaa A. Abdallah ◽  
Nawal A. El-Fishawy ◽  
Mohiy M. Hadhoud ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Block Based ◽  
Value Decomposition
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