Performance verification of the anisotropic diffusion technique for range image smoothing

1994 ◽  
Author(s):  
Manickam Umasuthan ◽  
Andrew M. Wallace
Keyword(s):  
Anisotropic Diffusion ◽  
Range Image ◽  
Image Smoothing ◽  
Performance Verification ◽  
Diffusion Technique

Dense range image smoothing using adaptive regularization

2000 ◽  
Author(s):  
Yiyong Sun ◽  
Joon-Ki Paik ◽  
J.R. Price ◽  
M.A. Abidi
Keyword(s):  
Range Image ◽  
Image Smoothing ◽  
Adaptive Regularization ◽  
Dense Range

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.

scholarly journals Enclosed Laplacian Operator of Nonlinear Anisotropic Diffusion to Preserve Singularities and Delete Isolated Points in Image Smoothing

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Zhiwu Liao ◽  
Shaoxiang Hu ◽  
Dan Sun ◽  
Wufan Chen
Keyword(s):  
Anisotropic Diffusion ◽  
Laplacian Operator ◽  
Image Smoothing ◽  
Unified Framework ◽  
Discretization Schemes ◽  
Edge Points ◽  
Isolated Points ◽  
Discretization Errors ◽  
Nonlinear Anisotropic Diffusion

Existing Nonlinear Anisotropic Diffusion (NAD) methods in image smoothing cannot obtain satisfied results near singularities and isolated points because of the discretization errors. In this paper, we propose a new scheme, named Enclosed Laplacian Operator of Nonlinear Anisotropic Diffusion (ELONAD), which allows us to provide a unified framework for points in flat regions, edge points and corners, even can delete isolated points and spurs. ELONAD extends two diffusion directions of classical NAD to eight or more enclosed directions. Thus it not only performs NAD according to modules of enclosed directions which can reduce the influence of traction errors greatly, but also distinguishes isolated points and small spurs from corners which must be preserved. Smoothing results for test patterns and real images using different discretization schemes are also given to test and verify our discussions.

A SELF-DUAL FILTERING TOGGLE OPERATOR FOR SPECKLE NOISE FILTERING

2013 ◽  
Vol 13 (03) ◽  
pp. 1350015 ◽  
Author(s):  
LEYZA BALDO DORINI ◽  
NEUCIMAR JERÔNIMO LEITE
Keyword(s):  
Image Processing ◽  
Real World ◽  
Anisotropic Diffusion ◽  
Experimental Tests ◽  
Speckle Noise ◽  
Noise Filtering ◽  
Morphological Filters ◽  
Edge Information ◽  
Diffusion Technique ◽  
Homogeneous Regions

In this work, we formalize an alternative way to build self-dual morphological filters that extends some results obtained for morphological centers to a different class of toggle operators. Thus, a wider range of primitives can be considered without causing oscillations, a common problem in toggle mappings. We also show that the combination of the morphological filters generated by using the proposed approach with the well-known anisotropic diffusion technique yields sound results where homogeneous regions are smoothed without degrading edge information. We explore the filtering of speckle noise, an interference pattern that causes a granular aspect in the image, thus limiting its interpretation and making it difficult further image processing tasks. Experimental tests on both synthetic and real-world images show promising results when compared to some well-known methods related to this type of filtering.

Fast Inpainting Algorithm of Woodblock New Year Pictures Damaged Area Based on Combination of Texture Distribution Analysis and Anisotropic Diffusion Technique

2015 ◽  
Vol 713-715 ◽  
pp. 1721-1724
Author(s):  
Wei Wei Yi
Keyword(s):  
Computational Complexity ◽  
Digital Image ◽  
Anisotropic Diffusion ◽  
Operation Time ◽  
Distribution Analysis ◽  
Traditional Methods ◽  
Diffusion Technique ◽  
Traditional Algorithm ◽  
Low Efficiency

According to the problem of New Year woodblock pictures damaged area in complicated cases and low efficiency of inpainting, fast inpainting algorithm based on combination of texture distribution analysis and anisotropic diffusion technique is proposed. To digital image of New Year woodblock pictures by using this algorithm to repair has similar effects with the traditional algorithm, but the operation time is far lower than traditional methods. Experiments show that this algorithm not only reduce the numerical computational complexity of the model and inpainting effect is good.

scholarly journals A Sub-band Anisotropic Diffusion Technique for de-speckling of Ultrasound Images of Breast Cancer

2021 ◽  
Author(s):  
Mayank Kumar Singh ◽  
Indu Saini ◽  
Neetu Sood ◽  
Jasleen Saini
Keyword(s):  
Breast Cancer ◽  
Anisotropic Diffusion ◽  
Imaging Technique ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Wavelet Thresholding ◽  
Large Dataset ◽  
Diffusion Technique ◽  
Extraction Step

Ultrasound imaging technique finds crucial application in clinical diagnosis of breast cancer. Presence of noise in ultrasound image due to different factor degrades the image quality and so the accuracy of diagnosis. Wavelet thresholding have been used from very beginning for de-noising of ultrasound image. Here in this paper we propose an intervention of anisotropic diffusion techniques in wavelet thresholding. In wavelet thresholding the thresholding operation usually applied after various feature extraction step, but in this study, we proposed to use a combinational approach. The approach reduces computational complexity of previous techniques. The proposed technique provides a Peak Signal to Noise Ratio of 28.46 and Mean Square Error of about 92.5537. The technique was practiced over large dataset of breast cancer images.

Sign in / Sign up

Export Citation Format

Share Document