Fractional-Order Anisotropic Diffusion for Image Denoising

2007 ◽  
Vol 16 (10) ◽  
pp. 2492-2502 ◽  
Author(s):  
Jian Bai ◽  
Xiang-Chu Feng
Keyword(s):  
Image Denoising ◽  
Fractional Order ◽  
Anisotropic Diffusion

Anisotropic Diffusion Model Based on a New Diffusion Coefficient and Fractional Order Differential for Image Denoising

2016 ◽  
Vol 16 (01) ◽  
pp. 1650003 ◽  
Author(s):  
Jianjun Yuan ◽  
Lipei Liu
Keyword(s):  
Diffusion Coefficient ◽  
Image Denoising ◽  
Diffusion Model ◽  
Fractional Order ◽  
Anisotropic Diffusion ◽  
Model Based ◽  
Proposed Model

This paper presents an improved anisotropic diffusion model which is based on a new diffusion coefficient and fractional order differential for image denoising. In the proposed model, the new diffusion coefficient can protect edges and fine characteristics from being over-smoothed. The fractional order differential is applied to weaken the staircasing effect, preserve fine characteristics. Additionally, the automatic set method of diffusion coefficient threshold is developed. Comparative experiments show that the proposed model succeeds in denoising and preserving fine characteristics.

Improved anisotropic diffusion ultrasound image denoising method based on logarithmic compression

2013 ◽  
Vol 32 (11) ◽  
pp. 3218-3220
Author(s):  
Jin YANG ◽  
Zhi-qin LIU ◽  
Yao-bin WANG ◽  
Xiao-ming GAO
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Ultrasound Image ◽  
Denoising Method

Low-Rank Estimation for Image Denoising Using Fractional-Order Gradient-Based Similarity Measure

2021 ◽  
Author(s):  
Zahid Hussain Shamsi ◽  
Dai-Gyoung Kim ◽  
Mukhtar Hussain ◽  
Rana Muhammad Bakhtawar Khan Sajawal
Keyword(s):  
Image Denoising ◽  
Fractional Order ◽  
Similarity Measure ◽  
Low Rank ◽  
Rank Estimation ◽  
Gradient Based

scholarly journals Image denoising using anisotropic second and fourth order diffusions based on gradient vector convolution

2012 ◽  
Vol 9 (4) ◽  
pp. 1493-1511 ◽  
Author(s):  
Huaibin Wang ◽  
Yuanquan Wang ◽  
Wenqi Ren
Keyword(s):  
Image Denoising ◽  
Fourth Order ◽  
Numerical Stability ◽  
Anisotropic Diffusion ◽  
Computational Cost ◽  
Gradient Vector ◽  
Order Model ◽  
Robustness To Noise ◽  
Fourth Order Model ◽  
Low Computational Cost

In this paper, novel second order and fourth order diffusion models are proposed for image denoising. Both models are based on the gradient vector convolution (GVC) model. The second model is coined by incorporating the GVC model into the anisotropic diffusion model and the fourth order one is by introducing the GVC to the You-Kaveh fourth order model. Since the GVC model can be implemented in real time using the FFT and possesses high robustness to noise, both proposed models have many advantages over traditional ones, such as low computational cost, high numerical stability and remarkable denoising effect. Moreover, the proposed fourth order model is an anisotropic filter, so it can obviously improve the ability of edge and texture preserving except for further improvement of denoising. Some experiments are presented to demonstrate the effectiveness of the proposed models.

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