Image denoising using modified nonlinear diffusion approach

2006 ◽  
Author(s):  
Akhilesh R. Upadhyay ◽  
Sanjay N. Talbar ◽  
Trimbak R. Sontakke
Keyword(s):  
Image Denoising ◽  
Nonlinear Diffusion

Image Denoising Algorithm with Nonlinear Diffusion Image Based on Region Homogeneity Measure

2009 ◽  
Vol 29 (6) ◽  
pp. 1530-1535
Author(s):  
毛玉星 Mao Yuxing ◽  
何为 He Wei ◽  
张占龙 Zhang Zhanlong
Keyword(s):  
Image Denoising ◽  
Nonlinear Diffusion

Nonlinear Diffusion Equation for Image Denoising Method Based on Gradient Fidelity Term

2010 ◽  
Vol 29-32 ◽  
pp. 934-939
Author(s):  
Da Sheng Wu ◽  
Qing Qing Wen ◽  
Yu Ping Rao
Keyword(s):  
Diffusion Equation ◽  
Image Denoising ◽  
Computational Efficiency ◽  
Nonlinear Diffusion ◽  
Functional Model ◽  
Nonlinear Diffusion Equation ◽  
Visual Effects ◽  
Denoising Method ◽  
Variation Function ◽  
Fidelity Term

This article introduces the gradient fidelity term into the functional model of the image denoising to obtain a new denoising functional model and derive the relative nonlinear diffusion denoising model. The new model has been proved that the bounded variation function is integrable, which will get rid of the problem of edge leaking. According to the experimental results, the application of this model is to prevent the "ladder" effect, the result of piecewise smooth can be acquired with more natural visual effects, meanwhile, the method has been proved more stable for the value calculation and has higher computational efficiency.

Nonlinear Diffusion Model for Fabric Image Denoising

2012 ◽  
Vol 627 ◽  
pp. 484-488
Author(s):  
Da Li Chen ◽  
Ding Yu Xue ◽  
Yang Quan Chen
Keyword(s):  
Image Denoising ◽  
Diffusion Model ◽  
Nonlinear Diffusion ◽  
Diffusion Models ◽  
Perceptual Comparison

In this paper, five nonlinear diffusion models for fabric image denoising are introduced. The advantages and drawbacks of these five models are described through describing their implementation methods. Quantitative and perceptual comparison experiments are given to verify the performance of these methods. Finally some valuable conclusions about denoising performance of these five models are present which is helpful for choosing and using these models in fabric image denoising.

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