Adaptive Anisotropic Diffusion for Image Denoising Based on Structure Tensor

2014 ◽  
Author(s):  
Kui Liu ◽  
Jieqing Tan ◽  
Benyue Su
Keyword(s):  
Image Denoising ◽  
Anisotropic Diffusion ◽  
Structure Tensor

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

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.

scholarly journals Despeckling PolSAR Images with a Structure Tensor Filter

2020 ◽  
Author(s):  
D Santana-Cedres ◽  
L Gomez ◽  
L Alvarez ◽  
Alejandro Frery
Keyword(s):  
Monte Carlo ◽  
Anisotropic Diffusion ◽  
State Of The Art ◽  
Monte Carlo Analysis ◽  
Structure Tensor ◽  
Synthetic Aperture ◽  
Experimental Setup ◽  
Polarimetric Synthetic Aperture Radar ◽  
Wishart Matrices ◽  
Dominant Direction

© 2004-2012 IEEE. In this letter, we propose a new despeckling filter for fully polarimetric synthetic aperture radar (PolSAR) images defined by 3× 3 complex Wishart distributions. We first generalize the well-known structure tensor to deal with PolSAR data which allows to efficiently measure the dominant direction and contrast of edges. The generalization includes stochastic distances defined in the space of the Wishart matrices. Then, we embed the formulation into an anisotropic diffusion-like schema to build a filter able to reduce speckle and preserve edges. We evaluate its performance through an innovative experimental setup that also includes Monte Carlo analysis. We compare the results with a state-of-the-art polarimetric filter.

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