• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.159-162
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• 2008

This paper presents a modified mean curvature diffusion filter to smooth speckle noise in images. Mean curvature diffusion filter has already shown good results in reducing noise in images while preserving fine details. In the mean curvature 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. In this paper, the diffusion coefficient is modified to be controlled adaptively by local image surface slope and heterogeneity. The local surface slope contributes to preserving details (e.g.edges) in image and the local surface heterogeneity helps the smoothing filter consider the amount of noise in both edge and non-edge area. The proposed filter's performance is demonstrated by quantitative experiments using speckle noised aerial image and TerraSAR-X satellite image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.163-172
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• 1996

Recently, it was reported that gaussian distribution and difference of two gaussians (DOG) to have band pass characteristics can be generated by simple iterative processes of the diffusion networks. In this paper, we propose method of improved implementation of a diffusion filter which can reduce total runing time, and operate by simple algorithm in contrast to the latest diffusion filter. We rebuild the diffusion network to a difference network which can generate DOG independently. Different filter characteristics are obtained just by each diffusion process and difference process. Quantitative analysis shows that the center frequency and the selectivity of each filter channel can be varied independently. Also, it would requires smaller amount of hardwares than conventioanl method to build a filter bank.

• Particle and aerosol research
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• v.11 no.2
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• pp.29-36
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• 2015

The purpose of this research is to find out the collection property of nanoparticle in diffusion filter to know particle size dispersion of nanomaterial using inertial force and principle of Brownian diffusion motion. We used inertial filters which are two different type and diffusion filters made by various kinds of Wiremesh and the different pieces of filter to compare with particle size distribution using NaCl particles. Finally, We made a conclusion as follows : (1) the bigger available charging volume is and the larger specific surface area of inertial filter is, the better collection efficiency is. (2) The higher wire-mesh number of filter is, the more collection efficiency of small particle is increasing because the wire of the higher Wiremesh number filter is thinner and denser. (3) The more pieces of wire-mesh filter, the more collection efficiency is increasing because it makes the residence time longer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.50-58
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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 Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.1036-1044
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• 1995

In this paper, a band-pass filter is implemented with the diffusion and difference processes by using the diffusion neural network model. The center frequency of this band-pass filter can be varied by iterations of the diffusion and difference operations, and the selectivity can be determined by iterations of the difference operation. We propose an efficient algorithm that can generate various band-pass filters using arbitrary diffusion and difference iterations. This algorithm needs only simple operations of diffusion and difference.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.21-28
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• 2015

The essential idea of de-noising is referring to neighboring pixels of a center pixel to be updated. Conventional adaptive de-noising filters use local statistics, i.e., mean and variance, of neighboring pixels including the center pixel. The drawback of adaptive de-noising filters is that their performance becomes low when edges are contained in neighboring pixels, while anisotropic diffusion de-noising filters remove adaptively noises and preserve edges considering intensity difference between neighboring pixel and the center pixel. The anisotropic diffusion de-noising filters, however, use only intensity difference between neighboring pixels and the center pixel, i.e., local statistics of neighboring pixels and the center pixel are not considered. We propose a new connectivity function of two adjacent pixels using statistics of neighboring pixels and apply connectivity function to diffusion coefficient. Experimental results using an aerial image corrupted by uniform and Gaussian noises showed that the proposed algorithm removed more efficiently noises than conventional diffusion filter and median filter.

• Journal of the Korean Society of Radiology
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• v.10 no.1
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• pp.45-51
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• 2016

The purpose of this study was tried to remove the noise and improve the spatial resolution in the computed tomography (CT) by using anisotropic 2-dimensional (2D) diffusion based filter. We used 4-channel multi-detector CT and american association of physicists in medicine (AAPM) phantom was used for CT performance evaluation to evaluate the image quality. X-ray irradiation conditions for image acquisition was fixed at 120 kVp, 100 mAs and scanned 10 mm axis with ultra-high resolution. The improvement of anisotropic 2D diffusion filtering that we suggested firstly, increase the contrast of the image by using histogram stretching to the original image for 0.4%, and multiplying the individual pixels by 1.2 weight value, and applying the anisotropic diffusion filtering. As a result, we could distinguished five holes until 0.75 mm in the original image but, five holes until 0.40 mm in the image with improved anisotropic diffusion filter. The noise of the original image was 46.0, the noise of the image with improved anisotropic 2D diffusion filter was decreased to 33.5(27.2%). In conclusion improved anisotropic 2D diffusion filter that we proposed could remove the noise of the CT image and improve the spatial resolution.

• Journal of Broadcast Engineering
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• v.16 no.3
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• pp.501-509
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• 2011

In this paper, we propose an edge profile adaptive bi-directional diffusion interpolation method which consists of shock filter and level set. In recent years many interpolation methods have been proposed but all methods have some degrees of artifacts such as blurring and jaggies. To solve these problems, we adaptively apply shock filter and level set method where shock filter enhances edge along the normal direction and level set method removes jaggies artifact along the tangent direction. After the initial interpolation, weights of shock filter and level set are locally adjusted according to the edge profile. By adaptive coupling shock filter with level set method, the proposed method can remove jaggies artifact and enhance the edge. Experimental results show that the average PSNR and MSSIM of our method are increased, and contour smoothness and edge sharpness are also improved.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.70-73
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• 2000

The error diffusion is good for reproducing continuous image to binary image. However the reproduction of edge characteristics is weak in power spectrum analysis of display error. It is suggested for us an edge-enhanced error-diffusion method that is included pre-processing algorithm for edge characteristic enhancement. Pre-processing algorithm is organized horizontal and vertical directional 2nd order differential values and weighting function of pre-filter. The improved Error diffusion using pre-filter, presents a good results visually which edge characteristics is enhanced. The performance of the proposed algorithm is compared with that of the conventional edge-enhanced error diffusion by measuring the RAPSD of display error, the egde correlation and the local average accordance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.20-28
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• 2000

The error diffusion algorithm is good for reproducing continuous image to binary image. However the reproduction of edge characteristic is weak in power spectrum analysts of display error. In this paper, an error diffusion method which include pre-filter algorithm for edge characteristic enhancement is proposed Pre-filter algorithm is organized horizontal and vertical directional differential value and weighting function of pre-filter First, it is obtained the horizontal and vertical differential value from the peripheral pixels in original image using $3{\times}3$ Sobel operator Secondly weighting function of pre-filter is composed by function including absolute value and sign of differential value The improved Error diffusion algorithm using pre-filter, present a good result visually which edge characteristic is enhanced. The difference between orignal image and halftoning image is compared with edge-enhanced error diffusion algorithm by measuring the radially averaged power spectrum density.