gaussian operator
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2021 ◽  
Vol 15 (3) ◽  
pp. 181-196
Author(s):  
Ge Zhang ◽  
Xiaoqiang Yan ◽  
Yulong Xu ◽  
Yangdong Ye

2020 ◽  
Vol 57 (12) ◽  
pp. 121004
Author(s):  
马永杰 Ma Yongjie ◽  
陈梦利 Chen Mengli

2019 ◽  
Vol 94 (11) ◽  
pp. 115101 ◽  
Author(s):  
Cheng Xiang ◽  
Yu-Jing Zhao ◽  
Shao-Hua Xiang

Author(s):  
Hongcai Li ◽  
Shixin Ma ◽  
Chuntong Liu ◽  
Hao Wang ◽  
Zhenxin He

2017 ◽  
Vol 5 (2) ◽  
pp. T151-T161 ◽  
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt

The interpretation of faults on 3D seismic data is often aided by the use of geometric attributes such as coherence and curvature. Unfortunately, these same attributes also delineate stratigraphic boundaries (geologic signal) and apparent discontinuities due to cross-cutting seismic noise. Effective fault mapping thus requires enhancing piecewise continuous faults and suppressing stratabound edges and unconformities as well as seismic noise. To achieve this objective, we apply two passes of edge-preserving structure-oriented filtering followed by a recently developed fault enhancement algorithm based on a directional Laplacian of a Gaussian operator. We determine the effectiveness of this workflow on a 3D seismic volume from central British Columbia, Canada.


2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shiping Guo ◽  
Hongqiang Lv ◽  
Yongyi Liu ◽  
Rongzhi Zhang ◽  
Jisheng Li

We focus on the multichannel image fusion problem for the purpose of reaching the diffraction-limited resolution of turbulence-degraded images observed by multiple acquisition channels. A hybrid strategy consisting of multichannel parallel deblurring followed by collaborative registration is developed for the final fusion. In particular, a Gaussian total variation regularization scheme taking advantage of low-order Gaussian derivative operators is proposed, which integrates the deblurring and registration problems into a unified mathematical formalization. Specifically, the gradient magnitude of Gaussian operator is proposed to define the total variation norm, and the Laplacian of Gaussian operator is used to adjust the regularization parameter when searching the extremum in each iterative step. In addition, the coordination technique involving the regularization parameter among different channels is also considered.


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