• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.147-153
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• 2014

In this paper, we present a non-dyadic lens distortion correction model and image restoration method based on local self-similarity to remove jagging and blurring artifacts in the peripheral region of the geometrically corrected image. The proposed method can be applied in various application areas including vehicle real-view cameras, visual surveillance systems, and medical imaging systems.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.137-140
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• 2000

카메라의 렌즈 등 광학장비의 성능 제한으로 인하여 초점이 맞지 않아 흐려지고 잡음으로 훼손된 영상을 복원하는데 일반적으로 반복복원방법이 사용된다. 이 경우에 가속변수는 훼손영상에 관계없이 영상전체에 일률적으로 적용되기 때문에 흐려짐 훼손이 심한 윤곽부분도 훼손이 작은 평면영역이 같이 일정하게 처리되어 수렴속도가 느려지고 시각적으로 중요한 윤곽부분의 복원에는 효율적이지 못하다. 이러한 문제점을 해결하기 위하여 본 논문에서는 흐려짐 훼손이 작은 평면영역은 가속변수를 작게하고 훼손이 큰 윤곽영역은 가속변수를 크게 하여 영상의 국부적인 특성에 따라 적응적으로 반복 복원하는 방법을 제안하였다. 제안한 복원방법은 기존의 방법과 비교하여 수렴속도가 빨라지고 시각적으로 중요한 윤곽정보의 복원에도 효율적임을 실험결과를 통해 할 수 있었으며, MSE면에서도 우수하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.5
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• pp.813-827
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• 2017

Surveillance cameras installed in tunnels capture the various video frames effected by dynamic and variable factors. In addition, localizing and managing the cameras in tunnel is not affordable, and quality of capturing frame is effected by time. In this paper, we introduce a new method to detect and track the vehicles in tunnel by using surveillance cameras installed in a tunnel. It is difficult to detect the video frames directly from surveillance cameras due to the motion blur effect and blurring effect on lens by dirt. In order to overcome this difficulties, two new methods such as Differential Frame/Non-Maxima Suppression (DFNMS) and Haar Cascade Detector to track cars are proposed and investigated for their feasibilities. In the study, it was shown that high precision and recall values could be achieved by the two methods, which then be capable of providing practical data and key information to an automatic accident detection system in tunnels.

• 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.