• 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.

• Journal of IKEEE
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• v.24 no.2
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• pp.497-501
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• 2020

Dehaze is essential for autonomous driving and intelligent CCTV to operate normally even in foggy weather. The method of airlight estimation is particularly important in dehaze technology. In this paper, we propose a patch-based airlight estimation algorithm and hardware structure that can reduce the amount of unnecessary computation and effectively estimate the airlight in various input images. Proposed algorithm is compared with the popular quad-tree method, and the hardware design is implemented by using XILINX's xc7z045-ffg900 target board as a structure that can satisfy to international standard 4K video in real time.

• 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 Engineers of Korea SP
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• v.49 no.1
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• pp.67-72
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• 2012

In this paper, we present an image enhancement method for atmospherically degraded images using atmospheric light and transmission based on color analysis. We first generate a normalized image using maximum value of each RGB color channel. Then, each atmospheric light is estimated from RGB color channel respectively by calculating reflectance of an image. We also, generate a transmission using gamma coefficients from the Y channel of the image. We can significantly enhance the visibility of an image by using the estimated atmospheric light and the transmission. The proposed algorithm can remove atmospheric degradation components better than existing techniques because the color prevents color distortion which is common problem of existing techniques. Experimental results demonstrate that the proposed algorithm can improve visibility be removing fog, smoke, and dust.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.118-127
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• 2017

We propose the dehazing algorithm which consists of two main parts, the derivation of the Atmospheric light and adaptive transmission map. In the getting the Atmospheric light value, we utilize the quad-tree partitioning where the depth of the partitioning is decided based on the difference between the averaged pixel values of the parent and children blocks. The proposed transmission map is adaptive for each pixel by using the parameter ${\beta}(x)$ to make the histogram of the pixel values in the map uniform. The simulation results showed that the proposed algorithm outperforms the conventional methods in the respect of the visual quality of the dehazed images and the computational complexity.

• 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 Platform Technology
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• v.11 no.6
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• pp.68-78
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• 2023

In this paper, the color distortion in images with haze would be analyzed. When haze is included in the scene, the color signal reflected in the scene is accompanied by color distortion due to the influence of transmittance according to the haze component. When the influence of haze is excluded by a conventional de-hazing method, the distortion of color tends to not be sufficiently resolved. Khoury et al. used the dark channel priority technique, a haze model mentioned in many studies, to determine the degree of color distortion. However, only the tendency of distortion such as color error values was confirmed, and specific color distortion analysis was not performed. This paper analyzes the characteristic of color distortion and proposes a restoration method that can reduce color distortion. Input images of databases used by Khoury et al. include Macbeth color checker, a standard color tool. Using Macbeth color checker's color values, color distortion according to changes in haze concentration was analyzed, and a new color distortion model was proposed through modeling. The proposed method is to obtain a mapping function using the change in chromaticity by step according to the change in haze concentration and the color of the ground truth. Since the form of color distortion varies from step to step in proportion to the haze concentration, it is necessary to obtain an integrated thought function that operates stably at all stages. In this paper, the improvement of color distortion through the proposed method was estimated based on the value of angular error, and it was verified that there was an improvement effect of about 15% compared to the conventional method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.489-490
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• 2015

객체의 인식을 위한 컴퓨터 비전 알고리즘은 안개와 비와 같은 기상이 좋지 않은 상황에서는 인식 성능이 떨어지고 있다. 이로 인하여 최근 악천후 환경에서 촬영된 영상으로부터 날씨 현상을 제거하는 기법들이 연구되고 있다. 빗줄기는 시공간적 무작위성으로 인하여 검출 및 제거가 어려운 현상이다. 또한 기존의 빗줄기 검출 및 제거 기법들은 대부분 고정된 카메라로부터 촬영된 영상을 대상으로 처리함으로써 자동차와 같은 움직임이 있는 촬영환경에서는 부적합하다. 최근에는 카메라나 객체의 움직임에 대응할 수 있는 빗줄기 검출 및 제거 알고리즘이 개발되고 있으나, 방대한 연산량이 필요하기 때문에 실시간이 불가능하다. 본 논문에서는 최근 연구되고 있는 카메라 움직임이 있는 환경에서 빗줄기 검출 및 제거 알고리즘을 DSP 환경에서 구현하고 내부 메모리 최적화와 EMDA 이용, 소프트웨어 파이프라인 등을 통해 최적화를 수행하여 실시간성을 보인다.

• Journal of IKEEE
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• v.24 no.2
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• pp.502-506
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• 2020

With the development of autonomous driving technology, the importance of object recognition technology is increasing. Haze removal is required because the hazy weather reduces visibility and detectability in object recognition. However, the image from which the haze has been removed cannot properly reflect the unique color, and a detection error occurs. In this paper, we use CIE1931 color coordinate system to extend or reduce the color area to provide algorithms and hardware that reflect the colors of the real world. In addition, we will implement hardware capable of real-time processing in a 4K environment as the image media develops. This hardware was written in Verilog and implemented on the SoC verification board.