• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1693-1702
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• 1999

This paper proposes the use of an adaptive Wiener filter for edge-preserving image filtering. Images are partitioned into a set of blocks of pixels which is divided into five subsets of blocks according to their edge contents and orientations. Each subset of blocks is used to define a covariance matrix, from which a Wiener filter is derived. Five covariance matrices and Wiener filters are thus obtained. An image-block classifier using the five sets of covariance matrices of the class is designed to classify each incoming block of pixels according to its edge content in the presence of noise. Experimental results are included to verify the usefulness of the proposed method.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.381-384
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• 2001

본 논문에서는 경계영역을 잘 보존할 수 있는 움직임 추정기법을 제안하였다. 고정크기 블록으로 움직임 추정시 생길 수 있는 경계영역에서의 왜곡은 인간의 시각에 민감하게 작용할 수 있다. 제안한 움직임 추정기법은 고정크기 블록기반으로서 기본적으로 MAD(Mean Absolute Difference)가 최소가 되도록 하는 동시에 영상의 경계값과 복잡도를 이용하여 경계부분에서 일어나는 시각적인 왜곡을 줄일 수 있도록 하였다. 제안한 움직임 추정기법은 기존의 경계영역 보존 기법에 비해 직관적 성능 및 주관적 차질이 향상됨을 모의 실험결과로부터 확인하였다.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.659-662
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• 2000

다양한 보간 기법을 정리해 보고 이를 통해서 기존의 보간 기법의 한계를 고찰해 본다. 보간의 효율성과 보간 결과 영상의 화질과는 Trade off 관계가 있으며, 이를 적절한 수준에서 결정하는 것은 중요한 문제이다. 본 논문에서는 Quadratic B-spline을 기저 함수로 하는 윤곽보존형 보간 필터를 사용한 영상확대 알고리즘을 제안한다. Unser의 Cardinal Cubic spline함수에 비해 적은 하드웨어만으로도 이상적인 저역 통과 필터의 특성을 가지며, 입력영상의 윤곽의 방향성을 고려한 적응적인 보간 기법의 적용으로 화질이 우수한 영상확대 알고리즘을 제안한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.59-67
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• 1992

Color image enhancement can be achieved easily by using linear form of coordinate system. But some popular color coordinate systems almost have nonlinear characteristics in the geometric form. In this paper, the proposed coordinate system has pseudo-linear form and based on IHS system which represents human color perception appropriately. And for the image intensity processing, an edge-preserving smoothing algorithm is presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.1
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• pp.88-96
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• 1989

General measure in the reliability is the k-terminal reliability, which is the probability that the specified vertices are connected by the working edges. To compute the k-terminal reliability components are usually assumed to be statistically independent. In this study the modeling and analysis of the k-terminal reliability are investigated when dependency among components is considered. As the size of the network increases, the number of the joint probability parameter to represent the dependency among components is increasing exponentially. To avoid such a difficulty the structured-event-based-reliability model (SERM) is presented. This model uses the combination of the network topology (physical representation) and reliability block diagram (logical representation). This enables us to represent the dependency among components in a network form. Computational algorithms for the k-terminal reliability in SERM are based on the factoring algorithm Two features of the ractoring algorithm are the reliability preserving reduction and the privoting edge selection strategy. The pivoting edge selction strategy is modified by two different ways to tackle the replicated edges occuring in SERM. Two algorithms are presented according to each modified pivoting strategy and illustrated by numerical example.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.90-102
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• 1993

In this paper, we propose a prgressive image transmission technique using hierarchical pyramid data structure which is constructed based on the projection data of an image. To construct hierarchical Gaussian pyramids, we first divide an image into 4$\times$4 subblocks and generate the projection data of each block along the horizontal, vertical, diagonal, and antidiagonal directions. Among images reconstructed by backprojecting the projection data along a single direction, the one giving the minimum distortion is selected. The Gaussian pyramid is recursively generated by the proposed algorithm and the proposed Gaussian images are shown to preserve edge information well. Also, based on the projection concept a new transmission scheme of the lowest Laplacian plane is presented. Computer simulation shows that the quantitative performance of the proposed pyramid coding technique using projection concept is similar to those of the conventional methods with transmission rate reduced by 0.1 ~ 0.2 bpp and its subjective performance is shown to be better due to the edge preserving property of a projection operation.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.4
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• pp.611-617
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• 2004

Pixel classification is one of basic image processing issues. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time. a pixel classification scheme based on image direction measure is proposed. As a typical application instance of pixel classification, an adaptive multi-level median filter is presented. An image can be classified into two types of areas by using the direction information measure, that is. smooth area and edge area. Single direction multi-level median filter is used in smooth area. and multi-direction multi-level median filter is taken in the other type of area. What's more. an adaptive mechanism is proposed to adjust the type of the filters and the size of filter window. As a result. we get a better trade-off between preserving details and noise filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.1011-1025
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• 2014

A demosaicing method for a Bayer-like W-RGB color filter array (CFA) is proposed. When reproducing images from a W-RGB CFA, conventional color separation methods for W-RGB CFA are likely to cause blurring near the edges due to rough averaging using a color ratio of neighboring pixels. Moreover, these methods cannot be applied to real-life digital cameras with W-RGB CFA because the methods were proposed under an ideal situation, W=R+G+B, not a real-life situation, $W{\neq}R+G+B$. To improve edge performance, we propose a method of constant color difference assumption with inversed weight, which uses information from all edge directions for interpolating all missing color channels. The proposed method calculates the correlation between W, R, G, and B to enable its application to real-life digital cameras with W-RGB CFA. Simulations were performed to evaluate the proposed method using images captured from a real-life digital camera with W-RGB CFA. Simulation results shows that we can demosaic by using the proposed algorithm compared with the conventional one in about +34.79% SNR, +11.43% PSNR, +1.54% SSIM and 14.02% S-CIELAB error. Thus, the proposed method demosaics better than the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3745-3761
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• 2020

Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.