• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1946-1952
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• 1999

A new image compression method to preserve edge characteristics in reconstructed images using an unsupervised learning neural is proposed in this paper. By the unsupervised competitive learning which generalizes previously proposed Centroid Neural Network(CNN) algorithm with the geometric characteristics of edge area and statistical characteristics of image data, more codevectors are allocated in the edge areas to provide the more accurate edges in reconstructed image. Experimental results show that the proposed method gives improved edge in reconstructed images when compared with SOM, Modified SOM and M/R-CNN.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.535-538
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• 2009

Speckle noise reduction for ultrasound CT image using morphological adaptive median filtering based on edge preservation is presented in this paper. Speckle noise is multiplicative feature and causes ultrasound image to degrade widely from transducer. An input image is classified into edge region and homogeneous region in preprocessing. The speckle is reduced by morphological operation on the 2D gray scale by using convolution and correlation, and edges are preserved. The adaptive median is processed to reduce an impulse noise to preserve edges. As the result, MAM of the proposed method enhances the image to about 10% in comparison with Winner filter by Edge Preservation Index and PSNR, and 10% to only adaptive median filtering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.12 no.4
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• pp.405-413
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• 1987

In this paper, we have discussed three-type median filters(SQUARE, CROSS, X-SHAPE) that preserving edge in an original image while reducing random noise was introduced for image enhancement and edge detection on radar image. Since radar image have a number of parts of curve, we compared results produced by edge detection operater proposed for improving the parts of curve with results of using the existing edge detection methods, such as Roberts, Sobel, Prewitt, Laplacian and Kirsch.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.439-441
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• 2005

A kernel regression filter with adaptive bandwidth is developed and successfully applied to digital reactivity meter for neutron signal measurement in nuclear reactors. The purpose of this work is not only reduction of the measurement noise but also the edge preservation of the reactivity signal. The performance of the filtering algorithm is demonstrated comparing with well known smoothing methods of conventional low-pass and bilateral filters. The developed method gives satisfactory filtering performance and edge preservation capability.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1424-1433
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• 1995

The edge detection process serves to simplify the analysis of images by drastically reducing the amount of data to be processed, while preserving useful structural informations about object boundaries. At first, this paper proposes an edge detection algorithm to reduce the amount of computation. The gradients of pixels are calculated by using first order differential equations on the pixels with even rows and even columns or odd rows and odd columns, and they are compared with a threshold to decide edges. As a result, the computational complexity is reduced to one third or one forth compared with the provious ones. To enhance the accuracy of edge detection, a method with the adaptive threshold for each pixel window which is calculated by using characteristic values is proposed. In this case, the performance can be improved since the threshold is calculated properly for each window according to the local characteristics of corresponding window.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.305-316
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• 2022

With the flood of digital data owing to the Internet of Things and big data, cloud service providers that process and store vast amount of data from multiple users can apply duplicate data elimination technique for efficient data management. The user experience can be improved as the notion of edge computing paradigm is introduced as an extension of the cloud computing to improve problems such as network congestion to a central cloud server and reduced computational efficiency. However, the addition of a new edge device that is not entirely reliable in the edge computing may cause increase in the computational complexity for additional cryptographic operations to preserve data privacy in duplicate identification and elimination process. In this paper, we propose an efficiency-improved duplicate data elimination protocol while preserving data privacy with an optimized user-edge-cloud communication framework by utilizing a trusted execution environment. Direct sharing of secret information between the user and the central cloud server can minimize the computational complexity in edge devices and enables the use of efficient encryption algorithms at the side of cloud service providers. Users also improve the user experience by offloading data to edge devices, enabling duplicate elimination and independent activity. Through experiments, efficiency of the proposed scheme has been analyzed such as up to 78x improvements in computation during data outsourcing process compared to the previous study which does not exploit trusted execution environment in edge computing architecture.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.241-244
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• 2005

Many building detection methods mainly rely on line segments extracted from aerial or satellite imagery. Building detection methods based on line segments, however, are difficult to succeed in high resolution satellite imagery such as IKONOS imagery, for most buildings in IKONOS imagery have small size of roofs with low contrast between roof and background. In this paper, we propose an efficient method to extract line segments and group them at the same time. First, edge preserving filtering is applied to the imagery to remove the noise. Second, we segment the imagery by watershed method, which collects the pixels with similar intensities to obtain homogeneous region. The boundaries of homogeneous region are not completely coincident with roof boundaries due to low contrast in the vicinity of the roof boundaries. Finally, to resolve this problem, we set up snake model with segmented region boundaries as initial snake's positions. We used a greedy algorithm to fit a snake to roof boundary. Experimental results show our method can obtain more .correct roof boundary with small size and low contrast from IKONOS imagery. Snake algorithm, building roof detection, watershed segmentation, edge-preserving filtering

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.2
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• pp.90-94
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• 2013

In this paper, we propose a new algorithm to improve the contrast ratio, to preserve information of bright regions and to maintain the color of backlight image that appears with a great relative contrast. Backlight images of the natural environment have characteristics for difference of local brightness; the overall image contrast improvement is not easy. To improve the contrast of the backlight images, MSR (Multi-Scale Retinex) algorithm using the existing multi-scale Gaussian filter is applied. However, existing multi-scale Gaussian filter involves color distortion and information loss of bright regions due to excessive contrast enhancement and noise because of the brightness improvement of dark regions. Moreover, it also increases computational complexity due to the use of multi-scale Gaussian filter. In order to solve these problems, a linear MSR is performed that reduces the amount of computation from the HSV color space preventing the color distortion and information loss due to excessive contrast enhancement. It can also remove the noise of the dark regions which is occurred due to the improved contrast through edge preserving filter. Through experimental evaluation of the average color difference comparison of CIELAB color space and the visual assessment, we have confirmed excellent performance of the proposed algorithm compared to conventional MSR algorithm.

• Journal of Astronomy and Space Sciences
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• v.15 no.1
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• pp.175-182
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• 1998

Any classification process using SAR images presupposes the reduction of multiplicative speckle noise, since the variations caused by speckle make it extremely difficult to distinguish between neighboring classes within the feature space. Therefore, several adaptive filter algorithms have been developed in order to distinguish between them. These algorithms aim at the preservation of edges and single scattering peaks, and smooths homogeneous areas as much as possible. This task is rendered more difficult by the multiplicative nature of the speckle noise the signal variation depends on the signal itself. In this paper, LEE(Lee 1908) and R-LEE(Lee 1981) filters using local statistics, local mean and variance, are applied to RADARSAT SAR images. Also, a new method of speckle filtering, EPOS(Edge Preserving Optimal Speckle)(Hagg & Sties 1994) filter based on the statistical properties of speckle noise is described and applied. And then, the results of filtering SAR images with LEE, R-LEE and EPOS filters are compared with mean and median filters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2490-2506
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• 2022

In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.