Learning an integral equation approximation to nonlinear anisotropic diffusion in image processing

1997 ◽  
Vol 19 (4) ◽  
pp. 342-352 ◽  
Author(s):  
B. Fischl ◽  
E.L. Schwartz
Author(s):  
Santosh Kumar ◽  
Nitendra Kumar ◽  
Khursheed Alam

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.


2019 ◽  
Vol 27 (1) ◽  
Author(s):  
César Bustacara-Medina ◽  
Leonardo Flórez-Valencia

Author(s):  
Andreas Schwarzkopf ◽  
Thomas Kalbe ◽  
Chandrajit Bajaj ◽  
Arjan Kuijper ◽  
Michael Goesele

2018 ◽  
Vol 66 ◽  
pp. 01016 ◽  
Author(s):  
Mateusz Zaręba ◽  
Tomasz Danek

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.


2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shaoxiang Hu ◽  
Zhiwu Liao ◽  
Dan Sun ◽  
Wufan Chen

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.


Sign in / Sign up

Export Citation Format

Share Document