scholarly journals Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yong Chen ◽  
Taoshun He
Keyword(s):  
Anisotropic Diffusion ◽  
Scale Space ◽  
Image Features ◽  
Spatial Gradient ◽  
Trigger Time ◽  
Local Variance ◽  
Series Of Experiments ◽  
Edge Indicator ◽  
Anisotropic Diffusion Equation ◽  
Space Filter

The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

Fractional-order difference curvature-driven fractional anisotropic diffusion equation for image super-resolution

2019 ◽  
Vol 10 (01) ◽  
pp. 1941012 ◽  
Author(s):  
Xuehui Yin ◽  
Shunli Chen ◽  
Liping Wang ◽  
Shangbo Zhou
Keyword(s):  
Diffusion Equation ◽  
Fractional Order ◽  
Anisotropic Diffusion ◽  
Super Resolution ◽  
Gauss Curvature ◽  
Order Difference ◽  
Image Super Resolution ◽  
Edge Indicator ◽  
Anisotropic Diffusion Equation ◽  
Difference Curvature

Image super-resolution methods-based existing edge indicating operators — namely Gauss curvature, mean curvature and gradient — cannot effectively identify the edges, ramps and flat regions and suffer from the loss of fine textures. To address these issues, this paper presents a fractional anisotropic diffusion equation based on a new edge indicator, named fractional-order difference curvature, which can characterize the intensity variations in images. We introduce the frequency-domain definition for fractional-order derivative by the Fourier transform, which is easy to implement numerically. The new edge indicator is better than the existing edge indicating operators in distinguishing between ramps and edges and can better handle the fine textures. Comparative results for natural images validate that the proposed method can yield a visually pleasing result and better values of MSSIM and PSNR.

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.

Image features using scale-space FAST corner detector and SURF descriptor

2014 ◽  
Vol 29 (4) ◽  
pp. 598-604
Author(s):  
王飞宇 WANG Fei-yu ◽  
邸男 DI Nan ◽  
贾平 JIA Ping
Keyword(s):  
Scale Space ◽  
Image Features

scholarly journals Stochastic analysis of image acquisition, interpolation and scale-space smoothing

1999 ◽  
Vol 31 (4) ◽  
pp. 855-894 ◽  
Author(s):  
Kalle Åström ◽  
Anders Heyden
Keyword(s):  
Stochastic Analysis ◽  
Traditional Approach ◽  
Image Acquisition ◽  
Scale Space ◽  
Image Features ◽  
Space Theory ◽  
Random Errors ◽  
Feature Detectors ◽  
Stochastic Properties ◽  
Discrete Scale

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. There has been a strong development in scale-space theory and its applications to low-level vision in the last couple of years. Scale-space theory for continuous signals is on a firm theoretical basis. However, discrete scale-space theory is known to be quite tricky, particularly for low levels of scale-space smoothing. The paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors.The modeling of image acquisition, image interpolation and scale-space smoothing is discussed, with particular emphasis on the influence of random errors and the interplay between the discrete and continuous representations. In doing so, new results are given on the stochastic properties of discrete and continuous random fields. A new discrete scale-space theory is also developed. In practice this approach differs little from the traditional approach at coarser scales, but the new formulation is better suited for the stochastic analysis of sub-pixel feature detectors.The interpolated images can then be analysed independently of the position and spacing of the underlying discretisation grid. This leads to simpler analysis of sub-pixel feature detectors. The analysis is illustrated for edge detection and correlation. The stochastic model is validated both by simulations and by the analysis of real images.

scholarly journals Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Hongjin Ma ◽  
Yufeng Nie
Keyword(s):  
Diffusion Model ◽  
Anisotropic Diffusion ◽  
Impulse Noise ◽  
Noise Removal ◽  
Image Features ◽  
Weighted Mean ◽  
Optimal Direction ◽  
Mean Filter ◽  
Mixed Noise ◽  
Mixed Noise Removal

A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

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.

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