A Time-Delay Regularization Based Diffusion Model for Image Denoising

Author(s):  
Qiang Wang ◽  
Feng WANG
IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 33568-33582 ◽  
Author(s):  
Na Wang ◽  
Yu Shang ◽  
Yang Chen ◽  
Min Yang ◽  
Quan Zhang ◽  
...  

2016 ◽  
Vol 19 (5) ◽  
pp. 1357-1374
Author(s):  
Yu-Tuan Lin ◽  
Yin-Tzer Shih ◽  
Chih-Ching Tsai

AbstractIn this paper we consider an anisotropic convection-diffusion (ACD) filter for image denoising and compression simultaneously. The ACD filter is discretized by a tailored finite point method (TFPM), which can tailor some particular properties of the image in an irregular grid structure. A quadtree structure is implemented for the storage in multi-levels for the compression. We compare the performance of the proposed scheme with several well-known filters. The numerical results show that the proposed method is effective for removing a mixture of white Gaussian and salt-and-pepper noises.


Author(s):  
Gao-Xiang Yang ◽  
Xiao-Yu Li

In this paper, a predator–prey reaction–diffusion model with Rosenzweig–MacArthur type functional response and spatiotemporal delay is investigated through using the tool of Turing bifurcation theories. First, by taking the average time delay as a bifurcation parameter, conditions of occurrence of Turing bifurcation are obtained through employing the Routh–Hurwitz criteria. Second, as the average time delay varies the amplitude equations of Turing bifurcation patterns including spots pattern and stripes pattern are also obtained through the multiple scale perturbation method. Finally, the two kinds of spatiotemporal evolution distributions of species such as spots pattern and stripes pattern are shown to illustrate theoretical results.


2012 ◽  
Vol 627 ◽  
pp. 484-488
Author(s):  
Da Li Chen ◽  
Ding Yu Xue ◽  
Yang Quan Chen

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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