• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.128-137
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• 2012

When a CCD camera acquires images in the low light level environment, not only the image signals but also noise components are amplified by the AGC (auto gain control) circuit. Since the noise level in the images acquired in the dark is very high, it is difficult to remove noise with existing denoising algorithms that are targeting the images taken in the normal light condition. In this paper, we proposed an adaptive denoising algorithm that can efficiently remove significant noises caused by the low light level. First, the window including a target pixel is transformed to the frequency domain. Then the algorithm compares the characteristics of equally divided four frequency bands. Finally the noises are adaptively removed according to the frequency characteristics. The proposed algorithm successfully improves the quality of low light level images than the existing algorithms do.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.437-440
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• 2022

In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

• 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 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.4
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• pp.141-148
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• 2008

This study was conducted to investigate the photosynthetic responses of four Oak species (Quercus mongolica, Q. serrata, Q. acutissima and Q. variabilis) by shading treatment. We investigated light response curve, photosynthesis (A)-intercellular $CO_2$ concentration (Ci) curve, leaf growth and chlorophyll content at the level of 35, 55 and 75% shading treatments and under the full sunlight. In our results, Q. variabilis and Q. acutissima showed increased leaf growth, chlorophyll content and net apparent quantum yield but reduced chlorophyll a/b and carboxylation efficiency under the low light intensity. Therefore, light absorption and light utilization efficiency were improved under the low light intensity. Q. mongolica showed the similar responses that Q. variabilis and Q. acutissima showed, but net apparent quantum yield was reduced. The effects of shading treatment on Q. serrata were lower than those of other three species.

• Journal of Bio-Environment Control
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• v.8 no.2
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• pp.108-114
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• 1999

This study was conducted to investigate the optimum environment for leaf lettuce (Lactuca sativa L. var. crispa) in a plant factory to increase mass-production efficiency of quality leaf lettuce. Transpiration rate and $CO_2$ assimilation rate were increased with increasing the photosynthetic photon flux density (PPFD). The highest fresh weight and dry weight were observed at the PPFD of 200 and 300 U moi $m^{-2}$ $s^{-l}$, respectively. The optimum aerial environment for the growth and quality of leaf lettuce in the plant factory was determined to be over 200 $\mu$mol $m^{-2}$ $s^{-1}$ for PPFB. Although the interaction between light intensity and nutrient level was not significant, the lettuce growth was the best under electrical conductivity (EC) of 1.8 mS $cm^{-1}$ / at high light intensity (250 $\mu$mol $m^{-2}$ $s^{-1}$ ) and EC of 2.4 mS cm-1 at low light level (150 $\mu$mol $m^{-2}$ $s^{-1}$ ) respectively.y.

• 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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.3
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• pp.314-321
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• 2024

Low road lighting is a lighting device that complements the shortcomings of existing pillar-type street lights. It is a lighting device that emits light from the side of the road surface and adjusts the luminance of the road surface like a light carpet. In this paper, to achieve full commercialization, we analyzed the luminance of the installed road surface and studied whether lighting could replace existing road lighting. In this study, the LMK (Luminance Measurement Camera) LABSOFT program was used to measure and analyze the surface luminance of road lighting, and the RELUX program was used to evaluate and analyze the simulation performance to determine light-based lighting conditions. A study was conducted to determine whether replacing pillar-type road lighting with low-level road lighting in a real environment would ensure comfortable and safe night vision for drivers at night.

• 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.