Fast Edge Preserving Smoothing Algorithm

Author(s):  
Ali Alsam ◽  
Hans Jakob Rivertz
Geophysics ◽  
2006 ◽  
Vol 71 (4) ◽  
pp. P5-P11 ◽  
Author(s):  
Nasher M. AlBinHassan ◽  
Yi Luo ◽  
Mohammed N. Al-Faraj

This paper presents a new algorithm for reducing noise in seismic-impedance cubes while preserving structural and stratigraphic discontinuities or edges. The method divides the vicinity of every location in a 3D impedance cube into a number of blocks as the analysis point moves throughout the volume. At an interior discontinuity location with any 3D orientation, the process does not average values across the edge because this would blur the feature. Instead, the smoothest neighboring value is used. The main advantage of this 3D smoothing algorithm is that it preserves both lateral and vertical edges (i.e., the impedance boundaries) and improves the contrast in coherence attributes derived from impedance data.


2017 ◽  
Vol 2017 (18) ◽  
pp. 123-129
Author(s):  
Takuma Kiyotomo ◽  
Keisuke Hoshino ◽  
Yuki Tsukano ◽  
Hiroki Kibushi ◽  
Takahiko Horiuchi

2020 ◽  
Vol 2020 (14) ◽  
pp. 294-1-294-8
Author(s):  
Sandamali Devadithya ◽  
David Castañón

Dual-energy imaging has emerged as a superior way to recognize materials in X-ray computed tomography. To estimate material properties such as effective atomic number and density, one often generates images in terms of basis functions. This requires decomposition of the dual-energy sinograms into basis sinograms, and subsequently reconstructing the basis images. However, the presence of metal can distort the reconstructed images. In this paper we investigate how photoelectric and Compton basis functions, and synthesized monochromatic basis (SMB) functions behave in the presence of metal and its effect on estimation of effective atomic number and density. Our results indicate that SMB functions, along with edge-preserving total variation regularization, show promise for improved material estimation in the presence of metal. The results are demonstrated using both simulated data as well as data collected from a dualenergy medical CT scanner.


2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6


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