• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3239-3267
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• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• 전자공학회논문지
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• 제51권8호
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• pp.51-57
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• 2014

본 논문은 비디오에서 안개 제거로 인한 플리커링(깜빡임) 현상을 효율적으로 제거하기 위한 방법을 제시한다. 단일 영상에서 사용하는 안개 제거 방법을 비디오 시퀀스에 그대로 적용하면, 안개제거를 위한 대기값이 매 프레임 마다 개별적으로 계산되기 때문에 플리커링 현상이 발생할 수 있다. 기존의 플리커링 현상을 제거하는 방법으로는 인접한 프레임간의 전달맵의 높은 상관관계 계수를 이용하는 방법이 존재한다. 하지만, 비디오 시퀀스에서 빈번하게 변경되는 대기값에 의해서도 플리커링 현상이 발생할 수 있기 때문에 인접한 프레임간의 상관관계를 효율적으로 고려한 대기값 추정방법이 필요하다. 본 논문에서는 안정적인 대기값 추정을 위해 평균값을 이용해 플리커링 현상을 제거하는 효율적인 안개 제거 기법을 제안한다. 실험결과 제안하는 방법이 기존의 방법보다 플리커링 현상을 줄여 좋은 성능결과를 보여 주는 것을 확인하였다.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.101-107
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• 2017

LabVIEW coding for video dehazing was developed. The dark channel prior proposed by K. He was applied to remove fog based on a single image, and K. B. Gibson's median dark channel prior was applied, and implemented in LabVIEW. In other words, we improved the image processing speed by converting the existing fog removal algorithm, dark channel prior, to the LabVIEW system. As a result, we have developed a real-time fog removal system that can be commercialized. Although the existing algorithm has been utilized, since the performance has been verified real - time, it will be highly applicable in academic and industrial fields. In addition, fog removal is performed not only in the entire image but also in the selected area of the partial region. As an application example, we have developed a system that acquires clear video from the long distance by connecting a laptop equipped with LabVIEW SW that was developed in this paper to a 100~300 times zoom telescope.