• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.33-36
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• 1998

Most image restoration problems are ill-posed and need to e regularized. A difficult task in image regularization is to avoid smoothing of image edges. In this paper, were proposed an edge-preserving image restoration algorithm using block-based edge classification. In order to exploit the local image characteristics, we classify image blocks into edge and no-edge blocks. We then apply an adaptive constrained least squares (CLS) algorithm to eliminate noise around the edges. Experimental results demonstrate that the proposed algorithm can preserve image edges during the regularization process.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.862-867
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• 2006

To restore image degraded by blur and additive noise in the optical and electrical system, a regularized iterative restoration is used. A regularization operator is usually applied to all over the image without considering the local characteristics of image in conventional method. As a result, ringing artifacts appear in edge regions and the noise is amplified in flat regions. To solve these problems we propose an adaptive regularization iterative restoration considering the characteristic of edge and flat regions using directional regularization operator. Experimental results show that the proposed method suppresses the noise amplification in flat regions, and restores the edge more sharply in edge regions.

• 전자공학회논문지 IE
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• v.44 no.1
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• pp.15-19
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• 2007

A study to have read does an improvement of an error restoration technology based on the edge direction interpolation that a stop image cared for inside frame correction more than with an image restoration way of a transfer error or with an aim. A way proposed to is based on edge direction detection method of a block utilizing the edge direction which will adjust a part damaged a sweater to a remaining part here. The rest of error pixel used non linear Midian filter for process later data information by the final stage and did interpolation. The examination result shows a good recuperation tendency and low accounts time of a way proposed to realization possibility of a real time image processing.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.726-731
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• 1986

An effective filtering algorithm which can reduce noise and preserve edges for the restoration of an image degraded by additive white Gaussian noise is presented. The algorithm proposed in this paper is an extension of Lee's algorithm modified to use local gradient information as well as local statistics. It does not require image modeling, and removes noise along the orientaiton of edges so that it does not blur the edge.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.141-147
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• 2000

In the restoration of degraded noisy motion blurred image, we have trade-off problem between smoothing the noise and restoration of the edge region. While the noise is smoothed, die edge or details will be corrupted. On the other hand, restoring the edge will amplify the noise. To solve this problem we propose an adaptive algorithm which uses I- H regularization operator for flat region and Laplacian regularization operator for edge region. Through the experiments, we verify that the proposed method shows better results in the suppression of the noise amplification in flat region, introducing less ringing artifacts in edge region and better ISNR than those of the conventional ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2541-2551
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• 1996

Human visual systems are sensitive to noise on the flat intensity area. But it becomes less sensitive on the edge area. Recently, many types of spatially adaptive image restoration methods have been proposed, which employ the above mentioned huan visual characteristics. The present paper presents an adaptive image restoration method, which increases sharpness of the edge region, and smooths noise on the flat intensity area. For edge detection, the proposed method uses the visibility function based on the local variance on each pixel. And it adaptively changes the regularization parameter. More specifically, the image to be restored is divided into a number of steps from the flat area to the edge regio, and then restored by using the finite impulse response constrained least squares filter.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.13 no.2
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• pp.80-94
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• 2010

The purpose of this study is suggest to restoration model of Pinus thunbergii in Saha-gu, Busan. The result of this study is summarized as follows. As the results of this study, vegetation restoration model is presented by separating community planting and edge planting. In community planting, as a group of canopy, there are 6 species; Pinus thunbergii, Quercus acutissima, Quercus dentata, Quercus serrata, Quercus alienna, Quercus variabilis. As a group of understory, there are 5 species; Platycarya strobilacea, Prunus sargentii, Styrax japonica, Eurya japonica, Morus bombycis. Also as a group of shrub, there were 15 kinds of species; Ulmus pavifolia, Ulmus davidiana, Lindera obtusiloba, Elaeagnus macrophylla, Mallotus japonicus, Ligustrum obtusifolium, Sorbus alnifolia, Rhus trichocarpa, Zanthoxylum schinifolium, Rosa wichuraiana, Rhus chinensis, Viburnum erosum, Rhododendron mucronulatum, Rhododendron yedoense, Indigofera pseudotinctoria. And as a group of edge vegetation, there were 10 kinds of species; Japanese Angelica, Symplocos chinensis, Pittosporum tobira, Lespedeza maximowiczii, Lespedeza bicolor, Rubus coreanus, Rubus idaeus, Vitis thunbergii, Ampelopsis brevipedunculata, Rosa multiflora. Vegetation restoration models of Pinus thunbergii community were calculated the units $400m^2$ for the average populations of the woody layer is 24 in canopy layer, 35 in understory layer, 410 in shrub layer, 34% herbaceous layer ground cover. And the average of breast-high area and canopy area is $10,852cm^2$ in canopy layer, in understory layer $1,546cm^2$, in shrub layer $1,158,660cm^2$. The shortest distance between trees is calculated as 2.0m in canopy layer, 1.9m in understory layer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.711-714
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• 2015

A new technique for image restoration using Weighted cross-shaped median filter with edge-detection algorithm is proposed in this paper. It consists of simple hypothesis test for edge-detection, and makes use of the weighted cross-shape window. This method is applied to noise corrupted image and its results are compared with those of median filters. As for the experimental result, method of weighted cross-shape median filter is superior to other median filters.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.103-110
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• 2015

In this paper, we propose a deblurring method using 1/4 selective filter. Deblurring methods require a lot of processing time for deblurring. In order to enhance execution speed, we propose a novel 1/4 selective filter. The proposed 1/4 selective filter restores major edge, but it distorts minor edge and texture. To solve this problem, we apply 1/4 selective filter to restore major edge and DOG(Difference of Gaussian) filter to restore minor edge and texture. Experimental results show that the proposed method removes the blur effectively.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.365-371
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• 2000

To restore image degraded by out-of-focus blur and additive noise, an iterative restoration is used. Acceleration parameter is usually applied equally to all over the image without considering the local characteristics of degraded images. As a result, the conventional methods are not effective in restoring severely degraded edge region and shows slow convergence rate. To solve this problem we propose an adaptive iterative restoration according to local degradation, in which the acceleration parameter has low value in flat region that is less degraded and high value in edge region that is more degraded. Through experiments, we verified that the proposed method showed better results with fast convergence rate, showed Visually better image in edge region and lower MSE than the conventional methods.