scholarly journals Image filtering using morphological amoebas

2007 ◽  
Vol 25 (4) ◽  
pp. 395-404 ◽  
Author(s):  
Romain Lerallut ◽  
Étienne Decencière ◽  
Fernand Meyer
Keyword(s):  
Image Filtering ◽  
Morphological Amoebas

scholarly journals SSVEP-EEG Denoising via Image Filtering Methods

2021 ◽  
pp. 1-1
Author(s):  
Wenqiang Yan ◽  
Chenghang Du ◽  
Yongcheng Wu ◽  
Xiaowei Zheng ◽  
Guanghua Xu
Keyword(s):  
Image Filtering

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

2021 ◽  
Author(s):  
Zhihua Gan ◽  
Xiuli Chai ◽  
Xiangcheng Zhi ◽  
Wenke Ding ◽  
Yang Lu ◽  
...  
Keyword(s):  
Image Filtering ◽  
Image Cipher ◽  
And Diffusion ◽  
Confusion And Diffusion

Guided image filtering based ℓ0 gradient minimization for limited-angle CT image reconstruction

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tianyi Wang ◽  
Chengxiang Wang ◽  
Kequan Zhao ◽  
Wei Yu ◽  
Min Huang
Keyword(s):  
Iteration Process ◽  
Real Data ◽  
Image Filtering ◽  
Reconstruction Technique ◽  
Ct Reconstruction ◽  
Imaging Device ◽  
Practical Applications ◽  
Guided Image Filtering ◽  
Simultaneous Algebraic Reconstruction Technique ◽  
Limited Angle

Abstract Limited-angle computed tomography (CT) reconstruction problem arises in some practical applications due to restrictions in the scanning environment or CT imaging device. Some artifacts will be presented in image reconstructed by conventional analytical algorithms. Although some regularization strategies have been proposed to suppress the artifacts, such as total variation (TV) minimization, there is still distortion in some edge portions of image. Guided image filtering (GIF) has the advantage of smoothing the image as well as preserving the edge. To further improve the image quality and protect the edge of image, we propose a coupling method, that combines ℓ 0 {\ell_{0}} gradient minimization and GIF. An intermediate result obtained by ℓ 0 {\ell_{0}} gradient minimization is regarded as a guidance image of GIF, then GIF is used to filter the result reconstructed by simultaneous algebraic reconstruction technique (SART) with nonnegative constraint. It should be stressed that the guidance image is dynamically updated as the iteration process, which can transfer the edge to the filtered image. Some simulation and real data experiments are used to evaluate the proposed method. Experimental results show that our method owns some advantages in suppressing the artifacts of limited angle CT and in preserving the edge of image.

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