• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.320-328
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• 1999

The Laplacian operator is usually used as a regularization operator which may be used as any differential operator in the regularization iterative restoration. In this paper, several kinds of differential operator and 1-H operator that has been used in our lab as well, as a regularization operator, were compared with each other. In the restoration of noisy motion-blurred images, 1-H operator worked better than Laplacian operator in flat region, but in the edge the Laplacian operator operated better. For noisy gaussian-blurred image, 1-H operator worked better in the edge, while in flat region the Laplacian operator resulted better. In regularization, smoothing the noise and resorting the edges should be considered at the same time, so the regions divided into the flat, the middle, and the detailed, which were processed in separate and compared their MSE. Laplacian and 1-H operator showed to be suitable as the regularization operator, while the other differential operators appeared to be diverged as iterations proceeded.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1685-1692
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• 2010

The Laplacian operator is usually used as a regularization operator which may be used as any differential operator in the regularization iterative processing. In this paper, several kinds of differential operator and proposed operator as a regularization operator were compared with each other performance. For noisy gaussian-blurred images, proposed operator worked better in the edge, while in flat region the conventional operator resulted better. In regularization, smoothing the noise and restoring the edges should be considered at the same time, so the regions divided into the flat, the middle, and the detailed, which were processed in separate and compared.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.1-8
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• 2004

In this paper, we propose a method for restoring trademark and caption areas using an isophote. In our method, the image restoration problem is modeled as an optimization problem, which in our case, is solved by a cost function with isophote constraint that is minimized using a GA The technique creates an optimal connection of all pairs of isophotes disconnected by a caption in the frame. For connecting the disconnected isophotes, we estimate the value of the smoothness, given by the best chromosomes of the GA and project this value in the isophote direction. Experimental results show that the isophote operator worked better than Laplacian operator for image restoration, and the proposed method has a great possibility for automatic restoration of a region in an advertisement scene.

• 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.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.247-251
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• 2001

영상을 처리하는 과정에서 광학시스템과 전기시스템의 특성으로 인해 흐려지고 잡음으로 훼손된 영상을 복원하는 경우에 일반적으로 정칙화 반복복원방법이 사용된다. 기존의 방법은 영상의 국부적인 특성을 고려하지 않고 영상전체에 일률적으로 정칙화 연산자를 사용함으로써 윤곽부분에서는 리플잡음을 초래하고 평면부분에서도 잡음증폭을 피할 수 없으며, 또한 시각적으로 효율적이지 못한 면이 있다. 본 논문에서는 이러한 문제점을 개선하기 위하여, 영상의 국부적인 특성을 고려하여 적응 정칙화 파라메타와 적응 정칙화 연산지를 사용하여 평면영역과 윤곽영역의 방향특성에 따라 적응적으로 처리하는 반복복원방법을 제안한다. 제안한 방법은 기존의 방법과 비교하여 평면영역에서의 잡음 평활화가 개선되고 시각적으로 중요한 윤곽부분 복원에 효율적임을 실험결과를 통해 알 수 있었으며 ISNR 면에서도 우수하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.91-99
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• 1994

We proposed the regularized iterative restoration method considering relaxation parameter and regularization paramenter in order to restore the noisy motion-blurred images. We used (i-H) as a regularization operator and these two kinds of constraints were applied while conventional regularization iterative restoration method proposed by Jan Biemond et al used the 2-D Laplacian filter and a predetermined regularization parameter value and relaxation parameter to 1. Through the experimental results, we showed better results compared with those by a conventional method and or regularized iterative restoration method just considering only a regularization parameter. These two kinds of constratints have good effects when applied into the regularized iterative restoration method for noisy motion-blurred images.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.643-645
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• 2002

본 논문은 영상의 isophote정보를 constraint로 사용만 유전자 알고리즘 (Genetic Algorithms) 기반의 컬러 영상 복원방법을 제안한다. 제안만 방법은 오염된 관측 영상으로부터 원 영상으로 복원하기 위해. 영상 복원 문제인 illposed 문제를 유전자 알고리즘을 이용하여 비용함수(cost funcition)가 최소가 되도록 하는 최적화 문제로 모델링 한다. 본 논문에서 제안만 방법은 영상에서 같은 밝기 값을 가진 영역의 경계선을 나타내는 isophole 를 비용함수의 정칙화(regularization) 연산자로 사용하여 영상을 복원한다. 사용자가 복원할 영역을 지정만 후, 유전자 알고리즘을 사용하여 복원될 영역치 isophote 를 자연스럽게 유지하도록 복원한다. 제안한 방법은 디지털 비디오에서 상업적인 광고나, 자막 측은 로고등을 제거하는데 사용될 수 있으며, 실험 결과, 일반적으로 영상 복원에 많이 사용하는 Constraint 로 라플라시안(Laplaoian) 연산자보다 isophote를 정칙화 연산자로 사용함으로써 효율적으로 영상이 복원됨을 알 수 있다.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.415-418
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• 2000

렌즈의 초점이 맞지 않아 흐려지고 잡음으로 훼손된 영상을 복원하는 경우에 일반적으로 정칙화 반복복원방법이 사용된다. 기존의 방법은 영상의 국부적인 특성을 고려하지 않고 영상전체에 일률적으로 정칙화를 행함으로써 윤곽부분에서는 리플잡음을 초래하고 평면부분에서도 잡음중폭을 피할 수 없으며, 또한 시각적으로 효율적이지 못한 면이 있다. 이러한 문제점을 개선하기 위하여, 본 논문에서는 영상을 방향이 없는 평면영역과 4가지 방향을 갖는 윤곽영역으로 나누어, 윤곽방향을 고려한 방향성 정칙화 연산자를 사용하여 평면영역과 윤곽영역의 방향특성에 따라 적응적으로 처리하는 반복복원방법을 제안한다. 제안한 방법은 기존의 방법과 비교하여 평면영역에서의 잡음 평활화가 개선되고 시각적으로 중요한 윤곽부분 복원에 효율적임을 실험결과를 통해 알 수 있었으며 ISNR 면에서도 우수하였다.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.1-6
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• 2009

It is very difficult to restore the images degraded by motion blur and additive noise. In conventional methods, regularization usually applies to all the images without considering local characteristics of the images. As a result, ringing artifacts appear in the edge regions and noise amplification is in the flat regions, as well. To solve these problems, we propose an adaptive iterative regularization method, using the way of regularization operator considering edge directions. In addition, we suggest an adaptive regularization parameter and an relaxation parameter. In conclusion, We have verified that the new method shows the suppression of the noise amplification in the flat regions, also does less ringing artifacts in the edge regions. Furthermore, it offers better images and improves the quality of ISNR, comparing with those of conventional methods.