• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.84-90
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• 2014

This paper presents the results of three-dimensional face point cloud smoothing based on a modified anisotropic diffusion method. The focus of this research was to obtain a 3D face point cloud with a smooth texture and number of vertices equal to the number of vertices input during the smoothing process. Different from other methods, such as using a template D face model, modified anisotropic diffusion only uses basic concepts of convolution and filtering which do not require a complex process. In this research, we used 6D point cloud face data where the first 3D point cloud contained data pertaining to noisy x-, y-, and z-coordinate information, and the other 3D point cloud contained data regarding the red, green, and blue pixel layers as an input system. We used vertex selection to modify the original anisotropic diffusion. The results show that our method has improved performance relative to the original anisotropic diffusion method.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.383-391
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• 2005

In this paper, a new anisotropic diffusion based on Alvarez, Lions, and Morel (ALM) diffusion model is proposed for the suppression of blocking artifact caused by discrete cosine transform (DCT) based image compression. The proposed diffusion model, which incorporates a 'rate control parameter' (RCP), makes it possible to reduce blocking artifacts while to preserve the edge. The RCP controls the rate between isotropic and anisotropic diffusion. Isotropic diffusion is encouraged to eliminate the blocking artifacts in a block boundary of a smooth region, while anisotropic diffusion is encouraged to keep the edge or texture sharp in edge and a block boundary within an edge region. Additionally, to avoid oversmoothness of the texture region, a 'speed control parameter' (SCP), which makes diffusion process slow in the texture region, is employed.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• Kyungpook Mathematical Journal
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• v.62 no.3
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• pp.557-581
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• 2022

The anisotropic-diffusion convection equation with exponentially variable coefficients is discussed in this paper. Numerical solutions are found using a combined Laplace transform and boundary element method. The variable coefficients equation is usually used to model problems of functionally graded media. First the variable coefficients equation is transformed to a constant coefficients equation. The constant coefficients equation is then Laplace-transformed so that the time variable vanishes. The Laplace-transformed equation is consequently written as a boundary integral equation which involves a time-free fundamental solution. The boundary integral equation is therefore employed to find numerical solutions using a standard boundary element method. Finally the results obtained are inversely transformed numerically using the Stehfest formula to get solutions in the time variable. The combined Laplace transform and boundary element method are easy to implement and accurate for solving unsteady problems of anisotropic exponentially graded media governed by the diffusion convection equation.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.87-94
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• 2003

Anisotropic diffusion is a selective smoothing technique that promotes smoothing within a region instead of smoothing across boundaries. In anisotropic diffusion, the rate of smoothing is controlled by the local value of the diffusion coefficient chosen to be a function of the local image gradient magnitude. El-Fallah and Gary E. Ford represented the image as a surface and proved that setting the inhomogeneous diffusion coefficient equal to the inverse of the magnitude of the surface normal results in surface evolving speed that is proportional to the mean curvature of the image surface. This model has the advantage of having the mean curvature diffusion (MCD) render invariant magnitude, thereby preserving structure and locality. In this paper, the proposed MCD model efficiently reduces diffusion coefficient at the thin edges using the smoothness of the surface.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.523-528
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• 2004

This study proposed an anisotropic diffusion restoration fer image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.436-450
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• 2017

Recently, the method based on fractional order partial differential equation has been used in image processing. Usually, the optional order of fractional differentiation is determined by a lot of experiments. In this paper, a denoising model is proposed based on adaptive fractional order anisotropic diffusion. In the proposed model, the complexity of the local image texture is reflected by the local variance, and the order of the fractional differentiation is determined adaptively. In the process of the adaptive fractional order model, the discrete Fourier transform is applied to compute the fractional order difference as well as the dynamic evolution process. Experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) of the proposed image denoising algorithm is better than that of other some algorithms. The proposed algorithm not only can keep the detailed image information and edge information, but also obtain a good visual effect.

• Fisheries and Aquatic Sciences
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• v.7 no.2
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• pp.90-95
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• 2004

An analytical model for predicting the convection-diffusion of solute dumped in a homogeneous open sea of constant water depth has been developed in a time-integral form. The model incorporates spatially uniform, uni-directional, mean and oscillatory currents for horizontal convection, the settling velocity for the vertical convection, and the anisotropic turbulent diffusion. Two transformations were introduced to reduce the convection-diffusion equation to the Fickian type diffusion equation, and then the Galerkin method was then applied via the expansion of eigenfunctions over the water column derived from the Sturm-Liouville problem. A series of calculations has been performed to demonstrate the applicability of the model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.45-49
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• 2004

An image enhancement method using modified anisotropic diffusion filter is proposed in this paper. It employs sensor noise estimation and scale space methods based on the minimum reliable scale. Then the anisotropic diffusion filter is modified by the calculated critical value function and local gradient. Through simulation, it is verified that the proposed algorithm has the capability of little or no noise amplification in homogenous region as well as superior edge enhancement.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.29-34
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• 2002

In this paper, we present a new geometric active contour model based on level set methods introduced by Osher and Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image. Using anisotropic diffusion filtering for each slice, we have the result with reduced noise and extracted exact shape. Volume rendering operates on three-dimensional data, processes it, and transforms it into a simple two-dimensional image.