scholarly journals Experimental Investigation of Anisotropic Diffusion Applied in Ghost Imaging Reconstruction

2020 ◽  
Vol 10 (18) ◽  
pp. 6437
Author(s):  
Xiaobin Gong ◽  
Min Tao ◽  
Gang Su ◽  
Baohua Li ◽  
Jian Guan ◽  
...  
Keyword(s):  
Anisotropic Diffusion ◽  
Noise Removal ◽  
Interference Term ◽  
Ghost Imaging ◽  
Initial Image ◽  
Noise Interference ◽  
Imaging Reconstruction ◽  
Image Edges ◽  
Anisotropic Diffusion Equation ◽  
Pseudo Inverse

In iterative pseudo-inverse ghost imaging (IPGI), how much the noise interference item of the current iteration approximates the real noise greatly depends on the clarity of initial image. In order to improve IPGI, we propose a method that introduces anisotropic diffusion to construct a more accurate noise interference term, where anisotropic diffusion adapts to both the image and the noise, so that it balances the tradeoff between noise removal and preservation of image details. In our algorithm, the anisotropic diffusion equation is used to denoise the result of each iteration, then the denoised image is used to construct the noise interference term for the next iteration. Compared to IPGI, our method has better performance in visual effects and imaging quality, as the image edges and details are better preserved according to the experimental results.

scholarly journals Local and Nonlocal Regularization to Image Interpolation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yi Zhan ◽  
Sheng Jie Li ◽  
Meng Li
Keyword(s):  
Anisotropic Diffusion ◽  
Image Interpolation ◽  
Real Image ◽  
Interpolation Model ◽  
Orthogonal Direction ◽  
Nonlocal Functional ◽  
Image Edges ◽  
Anisotropic Diffusion Equation ◽  
Nonlocal Regularization ◽  
Staircase Artifacts

This paper presents an image interpolation model with local and nonlocal regularization. A nonlocal bounded variation (BV) regularizer is formulated by an exponential function including gradient. It acts as the Perona-Malik equation. Thus our nonlocal BV regularizer possesses the properties of the anisotropic diffusion equation and nonlocal functional. The local total variation (TV) regularizer dissipates image energy along the orthogonal direction to the gradient to avoid blurring image edges. The derived model efficiently reconstructs the real image, leading to a natural interpolation which reduces blurring and staircase artifacts. We present experimental results that prove the potential and efficacy of the method.

A nonlinear anisotropic diffusion equation for image restoration with forward-backward diffusivities

2021 ◽  
Vol 14 ◽  
Author(s):  
Santosh Kumar ◽  
Nitendra Kumar ◽  
Khursheed Alam
Keyword(s):  
Image Processing ◽  
Anisotropic Diffusion ◽  
Diffusion Models ◽  
Smoothing Kernel ◽  
Proposed Model ◽  
Invariant Kernel ◽  
Nonlinear Anisotropic Diffusion ◽  
Anisotropic Diffusion Equation ◽  
Frequent Problem ◽  
Deblurring And Denoising

Background: In the image processing area, deblurring and denoising are the most challenging hurdles. The deblurring image by a spatially invariant kernel is a frequent problem in the field of image processing. Methods: For deblurring and denoising, the total variation (TV norm) and nonlinear anisotropic diffusion models are powerful tools. In this paper, nonlinear anisotropic diffusion models for image denoising and deblurring are proposed. The models are developed in the following manner: first multiplying the magnitude of the gradient in the anisotropic diffusion model, and then apply priori smoothness on the solution image by Gaussian smoothing kernel. Results: The finite difference method is used to discretize anisotropic diffusion models with forward-backward diffusivities. Conclusion: The results of the proposed model are given in terms of the improvement.

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

Pseudo-inverse Iterative Denoising Method for Object Reconstruction of Ghost Imaging

2017 ◽  
Vol 46 (2) ◽  
pp. 210001 ◽  
Author(s):  
张红伟 ZHANG Hong-wei ◽  
郭树旭 GUO Shu-xu ◽  
张驰 ZHANG Chi ◽  
羊超 YANG Chao ◽  
曹军胜 CAO Jun-sheng ◽  
...  
Keyword(s):  
Ghost Imaging ◽  
Object Reconstruction ◽  
Denoising Method ◽  
Iterative Denoising ◽  
Pseudo Inverse
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