• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.1985-1997
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• 1997

This paper proposed a regularized iterative image restoration by using method of conjugate gradient. Compared with conventional iterative methods, method of conjugate gradient has a merit to converte toward a solution as a super-linear convergence speed. But because of those properties, there are several artifacts like ringing effects and the partial magnification of the noise in the course of restoring the images that are degraded by a defocusing blur and additive noise. So, we proposed the regularized method of conjugate gradient applying constraints. By applying the projectiong constraint and regularization parameter into that method, it is possible to suppress the magnification of the additive noise. As a experimental results, we showed the superior convergence ratio of the proposed mehtod compared with conventional iterative regularized methods.

• 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.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.139-146
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• 1998

This paper proposes a regularized iterative image restoration using method of conjugate gradient considering a priori information. Compared with conventional regularized method of conjugate gradient, this method has merits to prevent the artifacts by ringing effects and the partial magnification of the noise in the course of restoring the image degraded by blur and additive noise. Proposed method applies the constraints to accelerate the convergence ratio near the edge portions, and the regularized parameter suppresses the magnification of the noise. As experimental results, I show the superior convergence ratio and the suppression by the artifacts of the proposed method compared with conventional methods.

• 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.

• 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 Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9B
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• pp.1588-1595
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• 2000

To restore image degraded by out-of-focus blur and additivie noise a regularized iterative restoration is used. In concentional method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive regularization iterative restoration using directional regularization operator considering edges in four directions and the regularization operator with on direction for flat regions. We verified that the proposed method show better results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions. As a result it showed visually better image and improved better ISNR further than the conventional methods.

• 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 Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.26-35
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• 2009

In this paper, we present morphology-based step region extraction and regularized iterative point-spread-function (PSF) estimation methods. The proposed PSF estimation method uses canny edge detector to extract the edge of the input image. We extract feasible vertical and horizontal edges using morphology analysis, such as the hit-or-miss transform. Given extracted edges we estimate the optimal step-response using flattening and normalization processes. The PSF is finally characterized by solving the equation which relates the optimal step response and the 2D isotropic PSF. We shows the restored image by the estimated PSF. The proposed algorithm can be applied a fully digital auto-focusing system without using mechanical focusing parts.

• 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.