Nonlocal Total Variation Denoising of Seismic Data

2013 ◽  
Author(s):  
S. Shang ◽  
L.G. Han
Keyword(s):  
Total Variation ◽  
Seismic Data ◽  
Nonlocal Total Variation ◽  
Total Variation Denoising

Simultaneous dictionary learning and denoising for seismic data

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. A27-A31 ◽  
Author(s):  
Simon Beckouche ◽  
Jianwei Ma
Keyword(s):  
Total Variation ◽  
Dictionary Learning ◽  
Seismic Data ◽  
Patch Size ◽  
Signal To Noise Ratio ◽  
Data Representation ◽  
Feature Preservation ◽  
Nonlocal Total Variation ◽  
Flexible Framework ◽  
Anisotropic Total Variation

We evaluated a dictionary learning (DL) method for seismic-data denoising. The data were divided into smaller patches, and a dictionary of patch-size atoms was learned. The DL method offers a more flexible framework to adaptively construct sparse data representation according to the seismic data themselves. The representation being learned from the data, did not rely on a guess of the data morphology like standard wavelet or curvelet transforms. The method could learn a dictionary and denoise seismic data, whether simultaneously or in two distinctive steps. Empirical study on field data showed promising denoising performance of the presented method in terms of signal-to-noise ratio and weak-feature preservation, in comparison with wavelets, curvelets, anisotropic total variation, and nonlocal total variation.

Compressed Sensing MRI using Curvelet Sparsity and Nonlocal Total Variation: CS-NLTV

2017 ◽  
Vol 2017 (13) ◽  
pp. 5-9 ◽  
Author(s):  
Ali Pour Yazdanpanah ◽  
Emma E. Regentova
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Nonlocal Total Variation

scholarly journals Exact solutions for the total variation denoising problem of piecewise constant images in dimension one

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Riccardo Cristoferi
Keyword(s):  
Exact Solution ◽  
Exact Solutions ◽  
Total Variation ◽  
Piecewise Constant ◽  
Geometrical Properties ◽  
Total Variation Denoising ◽  
Fidelity Term ◽  
Dimension One

AbstractA method for obtaining the exact solution for the total variation denoising problem of piecewise constant images in dimension one is presented. The validity of the algorithm relies on some results concerning the behavior of the solution when the parameter λ in front of the fidelity term varies. Albeit some of them are well-known in the community, here they are proved with simple techniques based on qualitative geometrical properties of the solutions.

Total variation denoising of probability measures using iterated function systems with probabilities

2017 ◽  
Author(s):  
Davide La Torre ◽  
Franklin Mendivil ◽  
Edward R. Vrscay
Keyword(s):  
Total Variation ◽  
Iterated Function Systems ◽  
Probability Measures ◽  
Iterated Function ◽  
Total Variation Denoising

Four-Directional Total Variation Denoising Using Fast Fourier Transform and ADMM

2018 ◽  
Author(s):  
Zhuyuan Cheng ◽  
Yuqun Chen ◽  
Lingzhi Wang ◽  
Fan Lin ◽  
Haiguang Wang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Total Variation ◽  
Total Variation Denoising

An adaptive fixed-point proximity algorithm for solving total variation denoising models

2017 ◽  
Vol 402 ◽  
pp. 69-81 ◽  
Author(s):  
Jin-He Wang ◽  
Fan-Yun Meng ◽  
Li-Ping Pang ◽  
Xing-Hua Hao
Keyword(s):  
Fixed Point ◽  
Total Variation ◽  
Total Variation Denoising

Tracking the Left Ventricle in Ultrasound Images Based on Total Variation Denoising

2007 ◽  
pp. 628-636 ◽  
Author(s):  
Jacinto C. Nascimento ◽  
João M. Sanches ◽  
Jorge S. Marques
Keyword(s):  
Left Ventricle ◽  
Total Variation ◽  
Ultrasound Images ◽  
Total Variation Denoising

The nonlocal total variation flow

2010 ◽  
pp. 163-189 ◽  
Author(s):  
Fuensanta Andreu-Vaillo ◽  
José Mazón ◽  
Julio Rossi ◽  
J. Julián Toledo-Melero
Keyword(s):  
Total Variation ◽  
Total Variation Flow ◽  
Nonlocal Total Variation
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