• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.24-29
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• 1999

In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.1
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• pp.111-118
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• 1996

In this paper, topographic edge points are detected from digital elevation model. Topographic edge points can be considered as the points which have rapid variation from its neighborhood. In this study, these points are dotected by Laplacian operator and noise reduction system is applied to remove small ripple. The results of this study show that Laplacian operator detects efficiently the topographic edge points, and that noise reduction system by local variance is efficient to remove small ripple but inefficient to reconstruct topographical features.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1903-1906
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• 2003

This paper proposes an adaptive regularized noise smoothing algorithm for range image using the area decreasing flow method, which can preserve meaningful edges during the smoothing process. Although the area decreasing flow method can easily smooth Gaussian noise, it has two problems; ⅰ) it is not easy to remove impulsive noise from observed range data, and ⅱ) it is also difficult to remove noise near edge when the adaptive regularization is used. In the paper, therefore, the second smoothness constraint is addtionally incorporated into the existing regularization algorithm, which minimizes the difference between the median filtered data and the estimated data. As a result, the Proposed algorithm can effectively remove the noise of dense range data with edge preserving.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.178-180
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• 2004

In this work, we propose new method of sigma filtering for efficient filtering and preserving edge regions in DCT Domain. In block-based image compression technique, the image is first divided into non-overlapping $8{\times}8$ blocks. Then, the two-dimensional DCT is computed for each $8{\times}8$ block. Once the DCT coefficients are obtained, they are quantized using a specific quantization table. Quantization of the DCT coefficients is a lossy process, and in this step, noise is added. In this work, we combine IDCT matrix and filter matrix to a new matrix to simplify filtering process to remove noise after IDCT in spatial domain, for each $8{\times}8$ DCT coefficient block, we determine whether this block is edge or homogeneous region. If this block is edge region, we divide this $8{\times}8$ block into four $4{\times}4$ sub-blocks, and do filtering process for sub-blocks which is homogeneous region. By this process, we can remove blocking artifacts efficiently preserving edge regions at the same time.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.130-136
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• 2001

Generally, a metal slitting saw is plain milling cutter with thickness less than 3/16 inch. This is used for cutting a workpiece that high dimensional accuracy and surface finish is necessary. A small thin milling cutter like a metal slitting saw is useful for machining a narrow groove. In this case, built up edge(BUE) is severe at each tooth and affects the surface integrity of the machined surface and tool wear. It is well known that tool geometry and cutting conditions are decisive factors to remove BUE. In this paper, we optimized the geometry of the milling cutter and selected cutting conditions to remove BUE by the experimental investigation. The experiment was planned with Taguchi method based on the orthogonal array of design factors such as coating, rake angle, number of tooth, cutting speed, feed rate. Response table was obtained from the number of built-up edge generated at tooth. The optimized tool geometry and cutting conditions could be determined through response table. In addition, the relative effect of factors was identified bh the analysis of variance (ANOVA). Finally, coating and cutting speed turned out important factors for BUE.

• Journal of Sensor Science and Technology
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• v.6 no.5
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• pp.376-384
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• 1997

In this paper, we propose a coding method of remotely sensed satellite image with edge region compensation. This method classifies each pixel vector considering spectral reflection characteristics of satellite image data. For each class, we perform classified intraband VQ and classified interband prediction to remove intraband and interband redundancies, respectively. In edge region case, edge region is compensated using class information of neighboring blocks and gray value of quantized reference bands. Then we perform classified interband prediction using compensated class information to remove interband redundancy, effectively. Experiments on LANDSAT-TM satellite images show that coding efficiency of the proposed method is better than that of the conventional methods.

• ETRI Journal
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• v.34 no.4
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• pp.564-571
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• 2012

This paper presents an algorithm for removing high-density impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line-weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.569-571
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• 1998

In this paper, we propose a method of restoring blurred images by an edge-sensitive adaptive filter. The direction of the edge is estimated using the properties of 2-D block FFT. Reduction of blurring due to the added noise during image transfer and the focus of lens caused by shooting a fast moving object is very important. To remove this phenomenon effectively, we can use the edge information obtained by processing the blurred images. The proposed algorithm estimates both the existence and the direction of the edge. On the basis of the acquired edge direction information, we choose the appropriate edge-sensitive adaptive filter, which enables us to get better images than images obtained by methods not considering the direction of the edge. The performance of the proposed algorithm is shown in the simulation result.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1565-1571
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• 2005

Images reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) algorithm have been observed to have checkerboard effects and have noise artifacts near edges as iterations proceed. To compensate this ill-posed nature, numerous penalized maximum-likelihood methods have been proposed. We suggest a simple algorithm of applying edge detecting process to the MLEM and Bayesian Expectation-Maximization (BEM) to reduce the noise artifacts near edges and remove checkerboard effects. We have shown by simulation that this algorithm removes checkerboard effects and improves the clarity of the reconstructed image and has good properties based on root mean square error (RMS).

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.55.4-55
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• 2002

In the semiconductor fabrication, particle contamination is wide-spread and one of major causes to yield loss. Extensive testing has revealed that even careful handling of wafers during processing may cause photo-resist materials to flake off wafer edges. So, to remove the photo-resist at the outer 5mm of wafers, UV(Ultraviolet) rays are exposed. WEE (Wafer Edge Exposure) process station is the system that exposes the wafer edge as prespecified by controlling the positioning mechanism and maintaining the light intensity level In this work, WEE process station has been designed so as to significantly lower the amount of particle contamination which occurs even during the most r...