• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.977-984
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• 2019

This paper proposes a single image dehazing that utilizes the transmission estimation with low complexity and the pixel-based JBDCP (Joint Bright and Dark Channel Prior) for the effective application of hazy outdoor images. The conventional transmission estimation includes the refinement process with high computational complexity and memory requirements. We propose the transmission estimation using combination of pixel- and block-based dark channel information and it significantly reduces the complexity while preserving the edge information accurately. Moreover, it is possible to estimate the transmission reflecting the image characteristics, by obtaining a different air-light for each pixel position of the image using the pixel-based JBDCP. Experimental results on various hazy images illustrate that the proposed method exhibits excellent dehazing performance with low complexity compared to the conventional methods; thus, it can be applied in various fields including real-time devices.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.201-204
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• 2009

본 논문에서는 한 장의 영상을 사용하여 영상 내에 존재하는 안개나 흐린 날씨 상태를 제거하는 알고리즘을 제안한다. 본 논문에서 제안하는 알고리즘은 dark channel prior를 사용하는 기법으로써 기존 알고리즘의 문제점을 보완하고 개선한다. 기존 알고리즘에서는 dark channel prior를 계산하여 전달량(transmission)을 추정한 후, 추정된 전달량을 영상의 모양에 맞추는 과정을 통해 정련된 전달량을 구한다. 본 논문에서는 추정된 전달량을 정련하는 과정을 개선함으로써 불필요한 메모리 사용량을 줄인다. 또한 계산량을 줄이기 위해 영상의 계층 분할을 이용한다. 실험 결과를 통하여 제안하는 알고리즘이 기존 알고리즘에 비해 개선된 성능을 발휘함을 확인한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.5
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• pp.645-652
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• 2015

In underwater, such as fish farm and sea, turbidity is increased by water droplets and various suspended, therefore light attenuation occurs depending on the depth also caused by the scattering effect of light float. In this paper, in order to improve the visibility of underwater images obtained from these aquatic environment, we propose a visibility enhancement method using a haze removal method based on dark channel prior and a trained color transform model. In order to train a color transform model, we used underwater pattern images captured from Pohang and Yeosu, and to measure the performance of the proposed method, we carried out experiment of visibility enhancement using underwater images collected from Yeosu, Geomundo and Philippines. The results show that the proposed method can improve the visibility of underwater images of various locations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.819-824
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• 2021

As technologies related to image processing such as autonomous driving and CCTV develop, fog removal algorithms using a single image are being studied to improve the problem of image distortion. As a method of predicting fog density, there is a method of estimating the depth of an image by generating a depth map, and various fog features may be used as training data of the depth map. In addition, it is essential to implement a hardware capable of processing high-definition images in real time in order to apply the fog removal algorithm to actual technologies. In this paper, we implement NLCV (Normalize Local Coefficient of Variation), a feature of fog based on coefficient of variation, in hardware. The proposed hardware is an FPGA implementation of Xilinx's xczu7ev-2ffvc1156 as a target device. As a result of synthesis through the Vivado program, it has a maximum operating frequency of 479.616MHz and shows that real-time processing is possible in 4K UHD environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1117-1124
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• 2018

Contrast enhancement plays an important role in various computer vision systems, since their usability can be improved with visibility enhancement of the images affected by weather and lighting conditions. This paper introduces a histogram modification algorithm that reflects the properties of original images in order to eliminate the saturation effect and washed-out of image details due to the over-enhancement. Our method modifies the original histogram so that an additive term fill histogram pits and the gamma correction suppresses histogram spikes. The parameters for the additive term and gamma correction are adjusted automatically according to statistical properties of the images. Experimental results for various low contrast and hazy images demonstrate that the proposed contrast enhancement improves visibility and reduces haze components effectively, while preserving the characteristics of original images, than the conventional methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1303-1310
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• 2021

We propose an edge-preserving transmission estimation by comparing the patch-based dark channel and the pixel-based dark channel near the edge, in order to improve the quality of outdoor images deteriorated by conditions such as fog and smog. Moreover, we propose a refinement that applies the Guided Image Filtering (GIF), a kind of edge-preserving smoothing filtering methods, to edges using Laplacian operation for natural restoration of image objects and backgrounds, so that we can dehaze a single image and improve the visibility effectively. Experimental results carried out on various outdoor hazy images that show the proposed method has less computational complexity than the conventional methods, while reducing distortion such as halo effect, and showing excellent dehazing performance. In It can be confirmed that the proposed method can be applied to various fields including devices requiring real-time performance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.1-7
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• 2019

This paper presents low-complexity single-image dehazing to enhance the visibility of outdoor images that are susceptible to degradation due to weather and environmental conditions, and applies it to various devices. The conventional methods involve refinement of coarse transmission with high computational complexity and extensive memory requirements. But the proposed transmission estimation method includes excellent edge-preserving performance from comparison of the pixel-based dark channel and the patch-based dark channel in the vicinity of edges, and transmission can be estimated with low complexity since no refinement is required. Moreover, it is possible to accurately estimate transmissions and adaptively remove haze according to the characteristics of the images via prediction of the atmospheric light for each pixel using joint bright and dark channel (JBDC). Comprehensive experiments on various hazy images show that the proposed method exhibits reduced computational complexity and excellent dehazing performance, compared to the existing methods; thus, it can be applied to various fields including real-time devices.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1911-1915
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• 2010

영상수위계는 카메라에 의해서 수위표를 촬영하여 촬영된 영상을 처리하여 수위값으로 변환하여 자동적으로 수위를 측정하는 장비이다. 이 수위계는 기존 수위측정 장비인 부자식, 압력식, 기포식, 초음파식, 레이다식과는 달리 수위표를 촬영한 영상으로부터 수위를 직접 눈으로 확인할 수 있는 장점이 있다. 이로 인해 영상자료로부터 측정된 수위를 검증할 수 있어 수위측정의 정확도를 향상시킬 수 있다. 그리고 수위표 영상과 더불어 관측지점 주변의 전체 영상을 동시에 촬영하여 실시간으로 전송하기 때문에 홍수시 하천 상황에 대한 모니터링 목적으로 사용될 수 있다. 수위관측용으로 운영 중인 영상수위계의 수위측정자료를 분석한 결과, 안개가 짙게 낀 경우, 목자판이 오염된 경우, 목자판 내에 그림자가 진 경우 등에서 수면을 인식하지 못하거나 오인식이 발생하였다. 이와 같이 수위 오 결측이 발생하는 경우에 수면을 정확하게 인식할 수 있도록 수위 측정방법을 개선하였다. 영상의 분할 이진화 처리기법, 노이즈 제거 기법, 목자판 영역내의 영상농도에 대한 히스토그램을 통해서 목자판과 수면을 구분하는 기법을 영상처리방법으로 새롭게 적용하였다. 수위 오 결측이 발생하는 영상자료를 이용하여 개선된 영상처리방법의 성능을 검증한 결과, 기존 방법으로는 수면을 전혀 인식하지 못하였던 영상이 개선된 방법을 적용하면 안개가 낀 경우에 약 97%까지, 목자판이 오염된 경우에 약 89%까지, 목자판 내에 그림자가 진 경우에 약 92%까지 수면을 인식하였다. 따라서 이와 같이 개선된 방법을 적용하게 되면 영상수위계의 정확도를 향상시킬 수 있을 것으로 기대된다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.269-279
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• 2020

Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.257-264
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• 2019

This paper presents a single image haze removal method via a pixel-based joint BDCP (bright and dark channel prior) and a hierarchical bilateral filter in order to reduce computational complexity and memory requirement while improving the dehazing performance. Pixel-based joint BDCP reduces the computational complexity compared to the patch-based DCP, while making it possible to estimate the atmospheric light in pixel unit and the transmission more accurately. Moreover the bilateral filter, which can smooth an image effectively while preserving edges, refines the transmission to reduce the halo effects, and its hierarchical structure applied to edges only prevents the increase of complexity from the iterative application. Experimental results on various hazy images show that the proposed method exhibits excellent haze removal performance with low computational complexity compared to the conventional methods, and thus it can be applied in various fields.