• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.697-705
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• 2015

This paper proposes a high-speed and high-quality haze removal method based on dual dark channels. In the conventional method, the halo artifacts are suppressed by the additional transmission refinement, but the transmission refinement is computationally intensive and the quality of the haze removal is sometimes unsatisfactory because of the residual halo artifacts. In the proposed method, the transmission is estimated with the mixture of the two dark channels with different window size. By mixing the two dark channels so as to avoid the halo artifacts, the proposed method realizes a high-quality haze removal even without the transmission refinement. Experimental results demonstrate that the quality of the results by the proposed method is superior to those by the conventional method and the speed of the haze removal is about 14.2 times higher than that of the conventional method.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.58-65
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• 2016

This paper presents optimization techniques to reduce the processing time of the dehazing method and proposes an efficient dehazing method based on them. In the proposed techniques, the atmospheric light is estimated based on the distributed sorting of the dark channel pixels, so as to reduce the computations. The normalization process required in the transmission estimation is simplified by the assumption that the atmospheric light is monochromatic. In addition, the dark channel is modified into the median dark channel in order to eliminate the transmission refinement process while achieving a comparable dehazing quality. The proposed dehazing method based on the optimization techniques is presented and its performance is investigated by developing a prototype system. When compared to the previous method, the proposed dehazing method reduces the processing time by 65% while maintaining the dehazing quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1477-1482
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• 2022

Haze is a local weather phenomenon in which very small droplets float in the atmosphere, and the amount and characteristics of haze may vary depending on the region. In particular, these haze reduce visibility, which can cause air traffic interference and vehicle traffic accidents, and degrade the quality of security CCTVs and so on. Therefore, in the past 10 years, research on haze removal has been actively conducted to reduce damage caused by haze. In this study, local haze removal is performed by weight generation using a haziness degree evaluator to adaptively respond to haze-free, homogeneous haze, and non-homogeneous haze cases. And the proposed method improves the limitations of the existing static haze removal method, which assumes that there is haze in the input image and removes the haze. We also demonstrate the superiority of the proposed method through quantitative and qualitative performance evaluations with benchmark algorithms.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.515-524
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• 2016

Infrared (IR) wavelength is out of visible range and thus usually cut by hot filters in general commercial cameras. However, some information from the near-IR (NIR) range is known to improve the overall visibility of scene in many cases. For example when there is fog or haze in the scene, NIR image has clearer visibility than visible image because of its stronger penetration property. In this paper, we propose an algorithm for fusing the RGB and NIR images to obtain the enhanced images of the outdoor scenes. First, we construct a weight map by comparing the contrast of the RGB and NIR images, and then fuse the two images based on the weight map. Experimental results show that the proposed method is effective in enhancing visible image and removing the haze.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.119-128
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• 2014

This paper presents a new method for improving the quality of images corrupted by an external source that generates an attenuation and scattering of light like dust, water droplets and fog. Conventional defog methods typically encounter a distortion such that the restored image has low contrast and oversaturation of color in some regions because of the mis-estimated airlight and wrong media transmission. Therefore, in order to mitigate these problems, we propose a robust airlight selection method and local saturation evaluation method for estimating media transmission. The proposed method addresses the wrong media transmission and over-saturation problems caused by the mis-estimated airlight and thereby improves the restored image quality. The results of relevant experiments of the proposed method against conventional ones confirm the improved accuracy of atmospheric light estimation and the quality of restored images with regard to objective and subjective performance measures.

• Journal of IKEEE
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• v.24 no.1
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• pp.243-247
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• 2020

As the autonomous driving market is rapidly growing, research on autonomous driving is being conducted. Self-driving functions should be performed regardless of the weather for the driver's safety. However, misty weather is difficult to autonomous driving because of the lack of visibility, so a defog algorithm should be used. The image obtained through the fog removal algorithm causes the image quality to deteriorate. To improve this problem, HSV color correction is used to increase the sharpness. In this paper, we propose a color correction hardware using HSV that can cope with 4K images. The hardware was designed with Verilog and verified by Modelsim. In addition, the FPGA was implemented with the goal of Xilinx's xc7z045-2ffg900.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• The Journal of Bigdata
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• v.6 no.2
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• pp.125-137
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• 2021

The number of CCTV installed in Korea is over 1.3 million, increasing by more than 15% annually. However, due to the limited budget compared to the installation demand, the infrastructure is composed of 500,000 pixel low-quality CCTV, and there is a limits on identification of objects in the video. Public CCTV has high utility in various fields such as crime prevention, traffic information collection (control), facility management, and fire prevention. Especially, since installed in high height, it works as its role in solving diverse crime and is in increasing trend. However, the current public CCTV field is operated with potential problems such as inability to identify due to environmental factors such as fog, snow, and rain, and the low-quality of collected images due to the installation of low-quality CCTV. Therefore, in this study, in order to remove the typical low-quality elements of public CCTV, the method of attenuating scattered light in the image caused by dust, water droplets, fog, etc and algorithm application method which uses deep-learning algorithm to improve input video into videos over quality over 4K are suggested.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.28-35
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• 2014

Surveillance cameras in national border and coastline area often occur the video distortion because of rapidly changing weather and light environments. It is positively necessary to enhance the distorted video quality for keeping surveillance. In this paper, we propose an adaptive video enhancement algorithm in the various environment changes. To solve an unstable performance problem of the existing method, the proposed method is based on Retinex algorithm and uses enhanced curves which is adapted in foggy and low-light conditions. In addition, we mixture the weighted HSV color model to keep color constancy and reduce noise to obtain clear images. As a results, the proposed algorithm improves the performance of well-balanced contrast enhancement and effective color restoration without any quality loss compared with the existing algorithm. We expect that this method will be used in surveillance camera systems and offer help of national defence with reliability.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.455-455
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• 2016

최근 기후변화로 빈번하게 발생하는 국지성 집중호우로 인해 홍수 피해가 증가하고 있으며, 이에 따라 유량 계측 자료의 필요성이 더욱 증대되고 있다. 현재까지 하천의 홍수 유량측정은 대부분 부자법에 의해 수행되어 왔지만, 측정 작업의 위험성이나 측정 정확도에 대해 여러 문제점이 지적되고 있다. 이에 비접촉식 측정 방법으로 안전하고 측정방식이 간편하며 높은 정확도를 갖춘 표면영상유속측정법(Surface Image Velocimetry, SIV)에 대한 연구가 활발하게 진행되고 있다. 다만 표면영상유속측정법의 경우 질 좋은 영상 촬영을 위해 밝은 빛이 필요하고, 일반적으로 매우 작은 규모의 하천을 제외하고는 영상 획득이 어렵다는 한계가 있다. 이와 같은 문제들을 해결하기 위해 최근 류권규 등(2015)은 영상 획득 장비로 원적외선 카메라의 적용성을 검토한 바 있다. 원적외선 카메라의 경우 별도의 조명을 필요로 하지 않기 때문에 주야간 구분 없이 사용 가능하다는 장점이 있으며 실제 하천에서 홍수와 함께 발생하는 안개의 영향 또한 받지 않아 고정식으로 설치하여 하천 유량측정 시스템을 구성하는 좋은 대안이 될 수 있음을 강조하였다. 다만, 원적외선 카메라는 야간에 적용시 주간과 비교하여 수표면의 움직임이 느리게 분석되는 경향이 있다고 하였다. 실험 결과를 보면, 소형 프로펠러 유속계로 측정한 수표면의 유속값에 비교하여 일반 캠코더 영상으로 산정한 유속 산정 결과의 상대오차는 최대 -10%인 반면, 원적외선 카메라 영상으로 산정한 유속 산정 결과의 오차는 -9%에서 -19%(주간), -10% 에서 -23%까지의 오차 범위를 나타내는 등 일반 캠코더에 비해 원적외선 카메라의 정확도가 다소 떨어지는 결과를 나타냈으며, 이러한 문제를 해결하기 위해서는 원적외선 영상의 명암값 분포 차이를 해결하기 위해 영상 처리 기법에 대한 추가적인 연구가 필요할 것이라고 하였다. 이에 본 연구에서는 원적외선 영상에 대한 다양한 영상 개선을 통해 표면영상유속계의 유속 측정 정확도를 높이고자 하였다. 이를 위해 우선, 적정 해상도와 시간간격을 제시하였으며, 영상의 색 보정과 영상 강화 등의 영상 개선을 통해 원적외선 영상을 이용한 유속 산정 정확도를 향상하였고, 마지막으로 다양한 야간 하천 흐름 조건에 적용하여 원적외선 영상을 활용한 표면영상유속계의 유속 측정 정확도를 높이고자 하였다.