Enhancement of flow-like structures in hyperspectral imagery using tensor nonlinear anisotropic diffusion

2011 ◽  
Author(s):  
Maider Marin-McGee ◽  
Miguel Velez-Reyes
Keyword(s):  
Anisotropic Diffusion ◽  
Hyperspectral Imagery ◽  
Nonlinear Anisotropic Diffusion

A nonlinear anisotropic diffusion equation for image restoration with forward-backward diffusivities

2021 ◽  
Vol 14 ◽  
Author(s):  
Santosh Kumar ◽  
Nitendra Kumar ◽  
Khursheed Alam
Keyword(s):  
Image Processing ◽  
Anisotropic Diffusion ◽  
Diffusion Models ◽  
Smoothing Kernel ◽  
Proposed Model ◽  
Invariant Kernel ◽  
Nonlinear Anisotropic Diffusion ◽  
Anisotropic Diffusion Equation ◽  
Frequent Problem ◽  
Deblurring And Denoising

Background: In the image processing area, deblurring and denoising are the most challenging hurdles. The deblurring image by a spatially invariant kernel is a frequent problem in the field of image processing. Methods: For deblurring and denoising, the total variation (TV norm) and nonlinear anisotropic diffusion models are powerful tools. In this paper, nonlinear anisotropic diffusion models for image denoising and deblurring are proposed. The models are developed in the following manner: first multiplying the magnitude of the gradient in the anisotropic diffusion model, and then apply priori smoothness on the solution image by Gaussian smoothing kernel. Results: The finite difference method is used to discretize anisotropic diffusion models with forward-backward diffusivities. Conclusion: The results of the proposed model are given in terms of the improvement.

scholarly journals An automatic stopping criterion for nonlinear anisotropic diffusion

2019 ◽  
Vol 27 (1) ◽  
Author(s):  
César Bustacara-Medina ◽  
Leonardo Flórez-Valencia
Keyword(s):  
Anisotropic Diffusion ◽  
Stopping Criterion ◽  
Nonlinear Anisotropic Diffusion

Volumetric Nonlinear Anisotropic Diffusion on GPUs

2012 ◽  
pp. 62-73 ◽  
Author(s):  
Andreas Schwarzkopf ◽  
Thomas Kalbe ◽  
Chandrajit Bajaj ◽  
Arjan Kuijper ◽  
Michael Goesele
Keyword(s):  
Anisotropic Diffusion ◽  
Nonlinear Anisotropic Diffusion

scholarly journals Nonlinear anisotropic diffusion techniques for seismic signal enhancing - Carpathian Foredeep study

2018 ◽  
Vol 66 ◽  
pp. 01016 ◽  
Author(s):  
Mateusz Zaręba ◽  
Tomasz Danek
Keyword(s):  
Anisotropic Diffusion ◽  
Structural Information ◽  
Seismic Signal ◽  
Image Processing Algorithm ◽  
Seismic Section ◽  
Significant Information ◽  
Diffusion Structure ◽  
Carpathian Foredeep ◽  
Nonlinear Anisotropic Diffusion ◽  
And Migration

The use of nonlinear anisotropic diffusion algorithm for advanced seismic signal processing in the complicated geological region of Carpathian Foredeep was examined. This technique allows for an improvement of seismic data quality and for more accurate interpretation by the recovery of a significant amount of structural information in the form of a correlating seismic reflections and by preserving true DHI indicators. It also allows searching for more subtle geological structures. Anisotropic diffusion is an iterative image processing algorithm that removes noise by modifying the data by solving partial differential equations. Moreover, it can reduce image noise without blurring the edges between regions of different chrominance or brightness. This filter preserves edges, lines, or other features relevant to the seismic structural and stratigraphic interpretation. The algorithm also enables noise reduction without removing significant information from a seismic section even for high dips values. For a better estimation of anisotropic diffusion structure tensor, the parameterization is done using the depth field and the calculations in the two-way travel time field. The presented research shows the results of using an anisotropic diffusion algorithm for post-stack and migration processing of seismic 3D data collected in Carpathian reservoir rocks of southern Poland.

scholarly journals A Numerical Method for Preserving Curve Edges in Nonlinear Anisotropic Smoothing

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shaoxiang Hu ◽  
Zhiwu Liao ◽  
Dan Sun ◽  
Wufan Chen
Keyword(s):  
Numerical Method ◽  
Anisotropic Diffusion ◽  
Digital Images ◽  
New Method ◽  
Image Smoothing ◽  
Time And Space ◽  
Curve Edge ◽  
Nonlinear Anisotropic Diffusion ◽  
Anisotropic Smoothing

We focus on nonlinearity for images and propose a new method which can preserve curve edges in image smoothing using nonlinear anisotropic diffusion (NAD). Unlike existing methods which diffuse only among the spatial variants, the new method suggests that the diffusion should be performed both among the time variants and spatial variants, named time and space nonlinear anisotropic diffusion (TSNAD). That is, not only the differences of the spatial variants should be estimated by the nearby spatial points but also the differences of the time variants should be approximated by the weighted time differences of nearby points, according to the differences of gray levels between them and the consideration point. Since the time differences of nearby points using NAD can find more points with similar gray levels which form a curve belt for the center pixel on a curve edge, TSNAD can provide satisfied smoothing results while preserving curve edges. The experiments for digital images also show us the ability of TSNAD to preserve curve edges.

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