• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.44-45
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• 2018

본 논문은 감마 보정 기반의 저조도 영상의 대조비 향상을 위한 최적의 계수 추정 기법을 제안한다. 제안하는 기법은 먼저 입력 영상의 휘도 정보를 로그 함수를 이용하여 정규화 한 후, 입력 영상을 밝은 부분과 어두운 부분으로 나눈다. 그런 다음 각각의 영역에서 통계적 특성을 고려한 비용 함수를 정의하고, 컨벡스 최적화 이론을 이용하여 최적의 감마보정 계수를 얻는다. 마지막으로 과포화 현상이 발생을 억제할 수 있는 색상 복원 기법을 적용한다. 컴퓨터 모의실험을 통해 제안하는 기법이 기존 기법에 비해서 낮은 계산 복잡도를 보이면서도 향상된 대조비를 보임을 확인한다.

• 전기의세계
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• v.66 no.10
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• pp.33-38
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• 2017

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.129-136
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• 2020

In this paper, we propose a thermal imagery-based object detection algorithm for low-light level nighttime surveillance system. Many features selected by Haar-like feature selection algorithm and existing Adaboost algorithm are often vulnerable to noise and problems with similar or overlapping feature set for learning samples. It also removes noise from the feature set from the surveillance image of the low-light night environment, and implements it using the lightweight extended Haar feature and adaboost learning algorithm to enable fast and efficient real-time feature selection. Experiments use extended Haar feature points to recognize non-predictive objects with motion in nighttime low-light environments. The Adaboost learning algorithm with video frame 800*600 thermal image as input is implemented with CUDA 9.0 platform for simulation. As a result, the results of object detection confirmed that the success rate was about 90% or more, and the processing speed was about 30% faster than the computational results obtained through histogram equalization operations in general images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.36-41
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• 2018

An image intensifying device is equipment that makes weak objects visible in a dark environment, such as making nighttime bright enough to let objects be visually observed. It is possible to obtain a clear image by amplifying the light in the presence of a certain amount of weak light. However, in an extremely low-light environment, where even moonlight is not present, there is not enough light to amplify anything, and the sharpness of the screen deteriorates. In this paper, a method is proposed to improve image quality by using multiple filters and anisotropic diffusion for output noise of the image-intensifying device in extreme low-light environments. For the experiment, the output of the image-intensifying device was obtained under extremely low-light conditions, and signal processing for improving the image quality was performed. The configuration of the filters for signal processing uses anisotropic diffusion after applying a median filter and a Wiener filter for effective removal of salt-and-pepper noise and Gaussian noise, which constitute the main noise appearing in the image. Experimental results show that the improvement visually enhanced image quality. Both peak signal-to-noise ratio (PSNR) and SSIM, which are quantitative indicators, show improved values.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.311-317
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• 2011

In this paper, we proposed realtime image enhancing method on the three noise types of input images, such as haze, low contrast and back light images. Some conventional de-hazing algorithms have good performance but need large memories and high computational burdens. We proposed the efficient algorithm which not only removes the haze but also reduces memory usage and computational complexity. We implemented the realtime system by using DM6446 DSP chip, and it showed the excellent result in these three problems; haze, low contrast and back light. We implemented the system with the processing speed at 15 frames/sec.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1680-1686
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• 2015

Edge detection is an essential preprocessing for most image processing application, and there are several existing detection methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) operators, etc. Those existing edge detection methods have not given satisfactory results since they do not offer enough pixel brightness change in low light level environment. Therefore, in this study new algorithms using brightness transfer function in the preprocessing and for edge detection applying standard deviation and average-weighted local masks are proposed. In addition, the performance of proposed algorithms was evaluated in comparison with the existing edge detection methods such as Sobel, Roberts, Prewitt, Laplacian, LoG operators.

• Journal of IKEEE
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• v.25 no.1
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• pp.139-147
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• 2021

This paper introduces an image decomposition-based deep learning method and loss function to improve low-light images. In order to remove color distortion and halo artifact, illuminance channel of an input image is decomposed into reflectance and luminance channels, and a decomposition-based multiple structural deep learning process is applied to each channel. In addition, a mixed norm-based loss function is described to increase the stability and remove blurring in reconstructed image. Experimental results show that the proposed method effectively improve various low-light images.

• Journal of IKEEE
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• v.25 no.2
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• pp.267-272
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• 2021

Recently, attention has been focused on renewable energy and carbon neutrality to resolve fossil energy depletion and environmental problems. In addition, high-rise urban buildings and an increase in building energy are rapidly increasing. There are many restrictions on installing solar power in urban areas. In addition, as buildings become taller, a lot of low-light environments in which shade is formed occur. Therefore, in this study, we intend to develop a power plant capable of generating electric power in an outdoor low-light environment and indoor lighting environment. The power plant in a low-light environment used a dye-sensitized solar cell. A unit cell and a 20cm×20cm module were manufactured, and the electrical characteristics of the power plant were measured using light sources of LED, halogen lamp, and 3-wavelength lamp. The photoelectric conversion efficiency of the unit cell was 17.2%, 1.28%, 19,2% for each LED, halogen lamp, and 3-wavelength lamp, and the photoelectric conversion efficiency of the 20cm×20cm module was 10.9%, 8.7%, and 11.8%, respectively. In addition, the maximum power value of the module was 13.1mW, 15.7 mW, and 14.2 mW for each light source, respectively, confirming the possibility of power generation in a low-light environment

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.741-749
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• 2022

Major space agencies are planning for the rover-based lunar exploration since water-ice was detected in permanently shadowed regions (PSR). Although sunlight does not directly reach the PSRs, it is expected that reflected sunlight sustains a certain level of low-light environment. In this research, the indoor testbed was made to simulate the PSR's lighting and topological conditions, to which low light enhancement methods (CLAHE, Dehaze, RetinexNet, GLADNet) were applied to restore image brightness and color as well as to investigate their influences on the performance of feature extraction and matching methods (SIFT, SURF, ORB, AKAZE). The experiment results show that GLADNet and Dehaze images in order significantly improve image brightness and color. However, the performance of the feature extraction and matching methods were improved by Dehaze and GLADNet images in order, especially for ORB and AKAZE. Thus, in the lunar exploration, Dehaze is appropriate for building 3D topographic map whereas GLADNet is adequate for geological investigation.